Post 2 — Evolutionary Theory Meets Academia: How Selection Shapes Scientific Methods

 In the first post of this series, we introduced a provocative idea: modern science is not just struggling — it is evolving in a direction that selects for weak, unreliable methods. This idea, central to Smaldino & McElreath’s influential 2016 paper The Natural Selection of Bad Science, rests on a powerful metaphor:

Scientific labs behave like organisms in an evolutionary system.
The methods they use are traits.
Their students are their progeny.
Publications are their fitness.

This metaphor isn’t just poetic. It is a rigorous conceptual framework allowing us to explain why poor research practices spread even when individuals have good intentions.

This post explores that evolutionary lens:

• Why does academia behave like an ecosystem?

• How do methods “reproduce”?

• What is the unit of selection?

• How do labs evolve over time?

• Why do certain practices become dominant while others vanish?

We will also use stories from the history of science — from Darwin’s own notebooks to Thomas Kuhn to modern lab politics — to illustrate how culture, training, and incentives act as evolutionary forces.

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1. The Central Analogy: Labs as Lineages

Smaldino & McElreath begin with a simple but profound observation:

Scientific practice is not created anew by each generation. It is inherited.

A PhD student or postdoc absorbs:

• their advisor’s habits,

• their lab’s methodological norms,

• statistical preferences,

• attitudes toward replication,

• openness to data sharing,

• norms about p-values,

• and even meta-level beliefs about what “counts” as good science.

When these trainees move on to establish their own labs, they carry those inherited traits with them, modifying them slightly, recombining them with influences from other labs, but largely preserving the lineage.

This is cultural evolution — a well-studied field — but applied here to scientific methodology.

Examples of inherited scientific culture

• The Cold Spring Harbor molecular biology lineage, which proliferated through shared summer courses and collaborative DNA work.

• The Copenhagen School of quantum mechanics, where Bohr’s philosophical stance became a transmittable “method” for thinking about physics.

• The Chicago School of economics, where rational-choice modeling spread through mentorship and institutional prestige.

Students didn’t just learn theories — they inherited methods, priorities, and epistemic values.

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2. What Exactly Is Being Selected? “Traits” in Scientific Lineages

Traits = Research practices.

Examples include:

• Sample sizes

• Statistical thresholds

• Willingness to preregister

• Commitment to replication

• How aggressively a lab chases significant results

• The balance between carefulness and productivity

• Whether negative results are ever written up

• How much time is spent refining experiments

These traits are not innate; they are learned.

And crucially — some traits boost short-term success at the expense of long-term reliability.

This immediately sets up a tension:

• Rigor is slow and costly.

• Speed produces more publishable results.

• Publishing more results increases grant success.

• Therefore, speed boosts evolutionary fitness, even if it lowers rigor.

This is exactly the type of trade-off natural selection thrives on.

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3. The Unit of Selection: The Lab, Not the Individual

Scientists often think of academic success as individual —
X scientists wins awards, publishes papers, secures grants.

But the evolutionary view shifts the focus:

The lab or research group is the unit of selection.

Why?

Because:

• Labs recruit students.

• Those students carry the lab’s practices elsewhere.

• Successful labs breed more “descendants.”

• Practices are copied and transmitted through mentoring lineages.

A lab that produces many successful students spreads its methods faster.
A lab that produces few students leaves little evolutionary footprint.

This is why certain laboratory cultures — good or bad — propagate with surprising persistence.

A historical anecdote

In early molecular biology, the Watson–Crick style of rapid, intuitive model-building spread widely, while Linus Pauling's more hierarchical and chemistry-heavy style faded.
Not because the former was inherently better, but because the labs carrying it produced more trainees in a rapidly expanding field.

Methods spread because trainees spread.

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4. Selection Pressure: Publication Counts as Fitness

In biological evolution, fitness = reproductive success.
In academic evolution, fitness = publication success.

The publication record determines:

• Who gets grants

• Who gets tenure

• Whose students get jobs

• Who attracts new students

• Which labs grow, split, and reproduce

Thus, labs with traits that maximize publication numbers reproduce more successfully in the academic ecosystem.

These traits may include:

• running many small-N studies

• chasing p < 0.05 results

• favoring novelty over accuracy

• avoiding replications

• presenting exploratory findings as confirmatory

• inflating claims

• streamlining the path to publication

In other words, questionable research practices (QRPs) increase fitness.

This is not a moral accusation.

It is an evolutionary prediction.

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5. Cultural Evolution in Action: Famous Examples

Example 1: Mendel vs. Fisher — Methodological Divergence

Mendel’s experiments are sometimes criticized for being too perfect.
Fisher famously suggested low-variance results indicated bias or over-tidying of data.

But more interesting is how Mendel’s meticulous methods did not spread.
His successors were not trained in his exacting style, and the field evolved into very different methodological norms.

Why?
Because the evolutionary environment around genetics changed.
Speed of discovery mattered more than perfection.

Example 2: The Replication Crisis in Psychology

For decades, psychology labs that produced:

• surprising effects

• clever paradigms

• small studies

• publishable results

…thrived.
These labs trained many students.

Meanwhile, labs that insisted on:

• high power

• robust replication

• slow, careful experimentation

…produced fewer papers and trained fewer students.

Over time, the field evolved toward flashy, unreliable results.

Example 3: Biomedical “breakthrough culture”

Preclinical cancer biology is notorious for irreproducibility.
Amgen reported in 2012 that they could reproduce only 6 of 53 “landmark” studies.

Why?
Because the labs producing “breakthroughs” got the funding.
They reproduced themselves.
Their methods spread.

Labs doing slow, confirmatory research did not grow.

Evolutionary selection at work.

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6. Why Good Practices Often Lose

In evolution, “good” = survival-enhancing, not morally good.

In academia:

• High-power studies (good for truth)
  require money, time, effort.

• Low-power studies (bad for truth)
  allow more experiments → more publications.

Thus:

Low power has higher fitness than high power.

This single insight explains much of the replication crisis.

Bad methods win not because they are bad, but because they:

• are cheaper,

• produce publishable results faster,

• generate more “offspring labs.”

Rigor is selected against.

This parallels biological evolution:

• Peacocks evolve burdensome tails because sexual selection rewards flashiness.

• Labs evolve burdensome statistical habits because academic selection rewards flashiness.

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7. Transmission: How Practices Spread Through Academic Pedigrees

Humans are cultural animals.
We copy behaviors with high social payoffs.
Science is no different.

The main transmission pathways:

1. Advisor → student

2. Collaborator → collaborator

3. Postdoc → new institution

4. Hiring committee → new faculty (selecting for “productive” candidates)

5. Grant panel → funded lab

This is similar to the transmission of:

• languages

• tool use in primates

• religious norms

• social rituals

• craft techniques

Scientific methodology is a cultural artifact.

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8. Drift, Mutation, Selection — All Present in Science

Smaldino & McElreath’s insight allows us to map biological evolutionary features directly onto academia.

Mutation: method innovations

Statistical innovations (Bayesian models, preregistration) are mutations.
Some spread, some die off.

Drift: accidental shifts

A charismatic advisor or influential journal editor can cause random swings in norms, independent of method quality.

Selection: survival of the most publishable

Traits that maximize output proliferate.

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9. A Closer Look: Why Evolutionary Thinking Helps Us Understand Scientific Decline

The evolutionary perspective clarifies several mysteries:

❓ Why do bad practices persist even though everyone agrees they are harmful?

➡ Because they increase fitness under current incentives.

❓ Why don’t reforms (e.g., “use bigger samples”) stick?

➡ Because selection pressure overrides idealistic norms.

❓ Why do some labs produce generations of similarly unreliable work?

➡ Because success breeds reproduction of method lineages.

❓ Why do fields diverge so much in reliability?

➡ Because each field has a different ecological niche:

• neuroscientists face expensive data collection → chronic low power

• social psychologists have flexible experiments → high p-hacking incentives

• physics has strong mathematical constraints → low methodological drift

Evolutionary environments differ.

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10. Conclusion: Science Evolves — but Who Directs That Evolution?

Viewing science as an evolutionary system reveals something uncomfortable:

We have created a selection environment in which poor methods thrive.

This doesn’t mean the scientists themselves are bad.
It means the system rewards the wrong traits.

Until we redesign the incentive structure, science will continue to evolve toward:

• more questionable practices

• more flashy but unreliable findings

• lower average rigor

• higher false positive rates

• declining public trust

In the next post, we’ll dive into the mathematical and computational models that Smaldino & McElreath use to demonstrate how bad science wins under current selection pressures.

Post 1 — The Crisis Beneath the Lab Coat: Why “Bad Science” Evolves

In 2016, Paul Smaldino and Richard McElreath published a striking and uncomfortable paper: “The Natural Selection of Bad Science.” It argues that science is not just failing in isolated pockets — it is evolving in a direction that systematically favors poor practices.

This is not because scientists are bad people. It’s because scientists are people inside an environment that rewards speed, flashiness, and positive results, regardless of whether those results are true.

This first post in our deep-dive series introduces the idea that bad science evolves, just like biological traits do — not through malice, but through selection pressures.

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1. What Exactly Is “Bad Science”?

“Bad science” doesn’t necessarily mean fraudulent science or outright misconduct. It means science that:

• uses underpowered studies,

• relies on weak statistical methods,

• employs p-hacking,

• selectively reports only significant results, and

• rarely replicates findings.

Such science can be performed by well-meaning researchers simply trying to survive in an academic ecosystem designed around publish-or-perish.

The replication crisis

The 2015 Open Science Collaboration attempted to replicate 100 psychology findings; only about 36% replicated. Similar failures have been seen in cancer biology and economics.

When replicability fails, it’s a sign that science is producing too many false positives, and doing so systematically.

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2. Why Are False Positives So Common?

False positives arise naturally from noise, but the modern scientific ecosystem amplifies them.

The incentives look like this:

Behavior	Reward
Publish flashy results quickly	Grants, tenure, fame
Take years to do a careful, high-power study	Very few rewards
Publish a null result	Often impossible
Do a replication	Actively discouraged

This creates a pressure cooker in which the quickest way to generate publishable results is simply to lower methodological standards.

As Ioannidis famously argued in 2005, “Most published research findings are false” — not because scientists are bad, but because the system selects for false-positive-generating behavior.

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3. Historical Anecdotes: When Incentives Tilt, Science Skews

The ESP Debacle

In 2011, psychologist Daryl Bem published a paper suggesting students could predict future events — ESP.
The methods were weak and statistically tortured, but the findings were novel and surprising. So they were published in a prestigious journal.

Why? Because novelty sells, even if the methods are flimsy.

The Brian Wansink “P-hacking Factory”

Wansink, a Cornell researcher, ran a social-nutrition lab famous for headline-grabbing results (“People eat more soup from self-refilling bowls!”).
His emails later revealed systematic data dredging — not fraud, but a culture where “find something publishable” trumped rigor.

These stories illustrate the paper’s thesis: labs that produce lots of positive results prosper, even if the results are fragile.

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4. Smaldino & McElreath’s Insight: Science Evolves Like a Darwinian System

Here’s the key insight of the paper:

Research methods are transmitted culturally through labs, and labs that publish more quickly produce more “descendant” labs.

Just as biological traits that increase reproductive fitness spread, research behaviors that increase publication output spread — regardless of whether they uncover truth.

This is the heart of the argument.

Labs = organisms

With traits such as:

• sample size norms

• statistical approaches

• replication habits

• degree of rigor

Students & postdocs = progeny

They carry the lab’s practices to new institutions.

Publication success = reproductive fitness

Thus, science becomes an evolutionary system — and not a benign one.

If quick-and-dirty methods generate more papers per year, they become dominant in the population of labs. Over decades, methodological deterioration becomes inevitable.

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5. The Model: Why Low-Power Science Wins

Smaldino & McElreath built computational models to test this idea. The models show that:

• Labs that use low sample sizes can run more studies.

• More studies = more chances for false positives (“significant results”).

• Significant results = publications.

• Publications = hiring, tenure, grants.

• Successful labs produce more trainees → spreading their methods.

In evolutionary terms:

Low-rigor labs have higher fitness.

This is an uncomfortable conclusion.

Is this really how academia works?

Yes — and you can see it empirically.

• Neuroscience has a median statistical power around 20–30%.

• Ecology has chronically tiny sample sizes.

• Biomedical research repeatedly fails in pharmaceutical replication checks (Amgen, Bayer).

These are not failings of individuals — they are signs of evolutionary pressure.

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6. Why Replication Fails as Quality Control

Replication is supposed to act like the immune system of science. But it almost never does.

Why?

• Replications are expensive.

• Replications are discouraged.

• Journals often reject replication papers.

• Senior scientists retaliate against negative replications.

Thus, poor methods do not get “punished.”
Instead, they persist and propagate.

The model shows that unless replication is made incredibly common and highly rewarded, it cannot counteract the evolutionary drift toward bad science.

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7. What This Means for the Future

If left unchanged, the system will continue to evolve toward:

• lower power

• higher false positive rates

• more irreproducible results

• faster publication cycles

• increased pressure on young scientists

• widening gap between published claims and reality

The paper is a warning: We are selecting for the worst kinds of science.

Unless incentives change, good methodology will go extinct in many fields.

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Conclusion: A Crisis of Evolution, Not of Ethics

Smaldino & McElreath force us to confront a difficult truth:

The decline in scientific rigor is not caused by bad people, but by a bad system.

Science is evolving — and not toward greater reliability.

But evolution is not destiny.
In later posts, we’ll explore how to redesign incentives so that good science becomes the winning strategy again.

Research as a Feeling: What Science Actually Feels Like

When we talk about research, we often describe it as a method, a discipline, a set of rules. We talk about protocols, replication, peer review, statistical significance.

But beneath the structure—beneath the grants and the deadlines and the unsolved problems—research is something far more intimate. It is a feeling.

It’s the pulse that scientists across history have recognized even when their worlds, tools, and fields were vastly different. Whether it was Rosalind Franklin staring down the helical shadows on her X-ray diffraction plate or Ramanujan scribbling mathematical visions in the early morning hours, that feeling—restless, luminous, stubborn—has always been the real engine of discovery.

And that is the feeling captured in this poem:

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Research as a Feeling

(Original Poem)

Research is not a task,
not truly—
it is the thrum beneath the ribs,
the quiet electricity
that wakes before you do.

It begins as a tremor,
a question so small it barely casts a shadow,
yet it rearranges the furniture
of your mind.

It is the warm ache
of finding a clue at midnight,
the way hope curls inside the chest—
soft, persistent—
like a creature learning to breathe.

It is frustration, too:
a slow-burn hunger,
a door that will not open
no matter how many keys you forge.
But even then, the door glows,
and you keep walking back to it.

Research is the feeling of standing
at the edge of a forest
where every leaf whispers a secret
you almost understand.
It is the echo of “almost”
that pulls you deeper.

It is falling in love
with the unseen,
with the possibility that truth
is a shape you can hold
if you learn how to cup your hands
just right.

It is the moment the data shifts
like dawn finding a window—
a clarity so sudden
you forget to breathe.

And then,
quietly,
you begin again.

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The Poem, Explained Through the Lives of Scientists

Let’s walk through the poem with real scientific stories that show research not as a career—but as an emotional landscape.

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“The thrum beneath the ribs… the quiet electricity that wakes before you do.”

Marie Curie used to say that she was often awake long before the sun, thinking about radium. She once admitted to a friend that the excitement of possibility made her feel “physically restless.”

For her, science wasn’t a job. It was physiological. A heartbeat. An electrical hum.

Many scientists recognize this: the feeling of waking up with a question already pressing against the mind. The poem opens by naming that sensation.

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“A question so small it barely casts a shadow… yet it rearranges the furniture of your mind.”

Charles Darwin’s entire life was changed by one small, almost inconspicuous question:
“Why do species vary from island to island?”

It wasn’t a grand philosophical inquiry at first. It was a tiny observation—finch beaks differing slightly across the Galápagos. But that small question shifted the mental architecture of biology forever.

Research often starts this way: a faint itch in the brain that slowly becomes a gravitational center.

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“The warm ache of finding a clue at midnight…”

Richard Feynman described how some of his best insights came “not during the day, but when I should have been asleep.”

Watson and Crick’s breakthrough moment came after a long night staring at cardboard cutouts of bases, finally realizing that A must pair with T, and C with G.

Midnight discoveries feel different. The world is quiet. Your thoughts echo louder. The poem captures the mixture of exhaustion and elation that only late-night research delivers.

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“It is frustration, too… a door that will not open no matter how many keys you forge.”

Every researcher knows this part.

Gregor Mendel spent years performing careful pea-plant experiments, only to have his work ignored during his lifetime. He faced the unopenable door of obscurity and scientific resistance.

Jocelyn Bell Burnell discovered the first pulsar but was initially dismissed outright—her signal was even jokingly called “LGM” for “Little Green Men.” She had to try key after key before the door cracked open for recognition.

Frustration is not the enemy of research. It is built into its architecture.

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“Standing at the edge of a forest where every leaf whispers a secret…”

This line evokes the feeling many scientists report at the beginning of a major, mysterious project.

Barbara McClintock described her genetic work in maize as “walking through a dark forest” where every discovery led to another branching path.

When the unknown feels vast but textured—full of quiet clues—you understand why researchers keep moving forward.

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“Falling in love with the unseen… truth as a shape you can hold.”

Einstein often wrote about his “almost romantic” pursuit of deep physical truths. He described falling in love not with results but with the hidden order of the universe.

And Ramanujan believed mathematical truths were “gifts” he could sense emotionally before he could prove them formally. To him, numbers were living things, and discovering them was an act of devotion.

This section of the poem captures that beautiful, irrational, almost spiritual part of research.

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“The moment the data shifts like dawn finding a window…”

Every scientist remembers that moment.

The gel with the unexpected band.
The graph where the curve finally rises.
The microscope slide where the pattern becomes obvious.
The code that produces a clean output for the first time.

For Kary Mullis, PCR came to him like a sudden sunrise during a nighttime drive—an abrupt alignment of clarity. He pulled off the road to scribble down the idea.

Discovery often feels like dawn: silent, sudden, transformative.

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“And then, quietly, you begin again.”

This is the most universal truth of research.

The project ends. The paper is published. The celebration lasts an hour or a day. And then the scientist returns to the bench, or the lab meeting, or the notebook—because the feeling that started everything is still alive.

Ada Lovelace described this cycle perfectly: “The more I know, the more I want to know.”

Research does not end. It loops.

And that is the quiet beauty of the poem’s final line.

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In the End: Research Is an Emotion Before It Is a Method

This poem reminds us that research is a human experience—full of longing, frustration, joy, surprise, obsession, and wonder.

It is not just a career path.
It is not just a skillset.
It is a feeling.

And across centuries, every scientist we admire has felt it too.

Research as a Feeling: A Hindi Poem and What It Teaches Us About Discovery

We often describe research in terms of methods, outputs, citations, and results. But behind every breakthrough lies something more fragile, more human—an emotion. Research is not just technique; it is a mood, a tension, a restlessness, a quiet joy.

That’s why I wrote a poem in Hindi capturing this inner landscape of inquiry. Hindi—with its softness, curves, and layered metaphors—carries emotion differently from English. The poem explores research not as an academic pursuit but as an intimate experience of curiosity, frustration, surrender, and illumination.

Below, you’ll find:

1. The entire Hindi poem

2. A line-by-line English translation

3. A brief explanation of the emotional idea behind each line

Let’s dive in.

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🌿 Hindi Poem: “अन्वेषण एक एहसास है”

अन्वेषण कोई काम नहीं,
वास्तव में नहीं—
यह पसलियों के नीचे धड़कती
वह धीमी कंपन है,
वह ख़ामोश बिजली
जो आपसे पहले जाग जाती है।

यह एक कंपन से शुरू होता है,
एक प्रश्न—इतना छोटा कि
अपनी परछाई भी ठीक से नहीं डालता,
फिर भी
आपके भीतर की पूरी सजावट
बदल देता है।

यह वह मधुर पीड़ा है
जब आधी रात को
कोई सुराग मिल जाए—
और उम्मीद सीने में
हल्के-हल्के सिमटकर
एक नये जीव की तरह
साँस लेना सीखने लगे।

यह हताशा भी है—
धीमी तपिश वाली भूख,
एक दरवाज़ा
जो कई चाबियाँ गढ़ने पर भी
नहीं खुलता।
पर वह दरवाज़ा चमकता रहता है,
और आप बार-बार
उसी ओर लौट आते हैं।

अन्वेषण उस जंगल के किनारे खड़े होने जैसा है
जहाँ हर पत्ता
कोई ऐसा रहस्य फुसफुसाता है
जिसे आप लगभग
समझ लेते हैं।
और वही “लगभग”
आपको और भीतर बुलाता है।

यह अनदेखे से प्रेम है—
उस संभावना से
कि सत्य एक ऐसा आकार है
जिसे आप थाम सकते हैं
यदि बस हाथों को
सही तरह मोड़ना सीख लें।

फिर वह पल आता है
जब आँकड़े बदलते हैं—
मानो भोर किसी खिड़की को
अचानक ढूँढ ले।
एक स्पष्टता—इतनी तेज़—
कि साँस रुक जाए।

और फिर,
बहुत शांति से,
आप फिर शुरू करते हैं।

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🔍 Line-by-Line Translation with Explanation

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1. “अन्वेषण कोई काम नहीं, वास्तव में नहीं—”

Translation: Research is not a task, not really.
Meaning: Research is not just labor or duty. It begins in emotion, not obligation.

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2. “यह पसलियों के नीचे धड़कती वह धीमी कंपन है,”

Translation: It is the quiet vibration beating beneath the ribs,
Meaning: Curiosity isn’t intellectual first—it’s physical. A restlessness that lives in the body.

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3. “वह ख़ामोश बिजली जो आपसे पहले जाग जाती है।”

Translation: the silent electricity that wakes before you do.
Meaning: Some questions come alive in you before you’re even fully awake—your mind is already working.

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4. “यह एक कंपन से शुरू होता है,”

Translation: It begins as a faint tremor,
Meaning: Great research often starts with something small—an intuition, a whisper.

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5. “एक प्रश्न—इतना छोटा कि अपनी परछाई भी ठीक से नहीं डालता,”

Translation: a question so small it barely casts a shadow,
Meaning: Not every important question looks big at the beginning.

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6. “फिर भी आपके भीतर की पूरी सजावट बदल देता है।”

Translation: yet it rearranges the entire interior of your mind.
Meaning: Even small questions can reshape how you think and see the world.

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7. “यह वह मधुर पीड़ा है जब आधी रात को कोई सुराग मिल जाए—”

Translation: It is that sweet ache when a clue arrives at midnight—
Meaning: Late-night insight combines exhaustion with joy; it’s painful and beautiful.

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8. “और उम्मीद सीने में हल्के-हल्के सिमटकर एक नये जीव की तरह साँस लेना सीखने लगे।”

Translation: and hope curls softly in your chest, learning to breathe like a newborn creature.
Meaning: New ideas feel fragile, precious—like something you must nurture.

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9. “यह हताशा भी है—धीमी तपिश वाली भूख,”

Translation: It is frustration too—a slow-burning hunger,
Meaning: Delayed answers can feel like hunger you can’t satisfy.

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10. “एक दरवाज़ा जो कई चाबियाँ गढ़ने पर भी नहीं खुलता।”

Translation: a door that won’t open no matter how many keys you forge.
Meaning: Failed attempts are part of the emotional cost of research.

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11. “पर वह दरवाज़ा चमकता रहता है, और आप बार-बार उसी ओर लौट आते हैं।”

Translation: yet that door keeps glowing, and you return to it again and again.
Meaning: The lure of understanding pulls you back despite frustration.

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12. “अन्वेषण उस जंगल के किनारे खड़े होने जैसा है जहाँ हर पत्ता कोई ऐसा रहस्य फुसफुसाता है जिसे आप लगभग समझ लेते हैं।”

Translation: Research is like standing at the edge of a forest where every leaf whispers a secret you almost understand.
Meaning: The unknown feels alive, full of hints—and “almost understanding” is intoxicating.

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13. “और वही ‘लगभग’ आपको और भीतर बुलाता है।”

Translation: and that very “almost” pulls you deeper inside.
Meaning: The gap between knowing and almost knowing drives exploration.

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14. “यह अनदेखे से प्रेम है—उस संभावना से कि सत्य एक ऐसा आकार है जिसे आप थाम सकते हैं,”

Translation: It is a love for the unseen—for the possibility that truth has a shape you can hold,
Meaning: Researchers fall in love with unseen patterns and hidden truths.

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15. “यदि बस हाथों को सही तरह मोड़ना सीख लें।”

Translation: if only you learn how to cup your hands just right.
Meaning: Mastery, skill, persistence—these are the tools to catch truth.

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16. “फिर वह पल आता है जब आँकड़े बदलते हैं—मानो भोर किसी खिड़की को अचानक ढूँढ ले।”

Translation: Then comes that moment when the data shifts—as if dawn suddenly finds a window.
Meaning: Discovery feels like sudden light entering the mind.

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17. “एक स्पष्टता—इतनी तेज़—कि साँस रुक जाए।”

Translation: a clarity—so sharp—you forget to breathe.
Meaning: True insight is breathtaking, literally.

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18. “और फिर, बहुत शांति से, आप फिर शुरू करते हैं।”

Translation: And then, quietly, you begin again.
Meaning: Research is cyclical. Every answer births a new question.

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🌟 Why This Poem Matters

This poem tries to remind us of something many scientists forget:
Research is not merely a profession. It is an emotional state.
It is hunger, fascination, surrender, patience, and renewal.
It is as human as love.

Uneven Evolution: Why the Global South Still Struggles to Teach the Science of Change

An opinion piece by evolutionary biologist Amitabh Joshi, titled  “We the Living: India badly needs more, not less, evolutionary biology” in the Deccan Chronicle (2018), raised concerns about the state of evolutionary biology in India’s academic and educational landscape. The article argued that evolutionary biology remains underrepresented in both teaching and research, despite being central to understanding biological systems.

Key ideas from the article

The piece was written in response to a public debate following a ministerial comment questioning Darwinian evolution in school curricula. While the controversy was short-lived, Joshi used it to highlight deeper systemic issues in science education.

According to the article:

• Evolutionary biology is taught only superficially in many Indian schools and universities, often treated as a minor topic compared to molecular biology, genetics, or biotechnology.

• Evolution serves as a conceptual framework connecting different areas of biology, rather than being just another branch of it — “biology without evolution would be like chemistry without the periodic table.”

• Understanding evolution has wide applications, including in epidemiology, agriculture, drug resistance, and conservation.

• There are few institutions in India that offer specialized training in evolutionary biology. The Evolutionary and Organismal Biology Unit at JNCASR was one of the few dedicated programmes, but its integrated PhD track was discontinued in 2016.

• Evolutionary biology research, the article noted, is not necessarily expensive, meaning that state universities could build capacity with modest resources.

The author concluded that India needs both curriculum reform and institutional support — including the creation of at least one national centre devoted to postgraduate training and research in evolutionary biology.

Broader context in the Global South

The challenges described in the article are not unique to India. Studies from other countries in the Global South point to similar trends. For instance, a study in Brazil found that only about 44% of biology students accepted a fully naturalistic view of evolution, while others favored a creationist or mixed framework (Evolution: Education and Outreach, 2017). Common features across many Global South countries include: Limited integration of evolution in school and university curricula. Cultural or religious factors influencing how evolution is taught or perceived. Concentration of funding in molecular or applied life sciences, leaving fewer resources for conceptual or field-based evolutionary studies. India shares some of these structural issues but also has distinctive advantages — a rich biodiversity, varied ecological systems, and opportunities for field-based evolutionary research. Strengthening this field could therefore have both national and global relevance.

Comparison with the West

In contrast, universities in North America, Europe, and Australia generally treat evolutionary biology as a core part of life sciences. Many have dedicated departments or interdisciplinary programs linking evolution with medicine, psychology, and genomics. For example, most major U.S. universities host departments or graduate schools of “Ecology and Evolutionary Biology,” which facilitate both teaching and research in this area. 

There are also efforts to embed evolutionary principles into diverse fields — from public health to sustainability — supported by data showing that interdisciplinary teaching improves understanding and acceptance of evolution (Evolution: Education and Outreach, 2023). Even so, debates about evolution and public understanding persist in parts of the Western world, showing that education and communication around evolutionary science remain ongoing global challenges.

 

Emerging directions

The discussion around India’s position in evolutionary biology connects to wider conversations in science policy and education reform. Some recurring ideas include:

• Integrating evolutionary concepts into all levels of biology curricula.

• Expanding postgraduate programmes and research centres dedicated to evolutionary science.

• Encouraging interdisciplinary research linking evolution with medicine, agriculture, and conservation.

• Building international collaborations to leverage India’s biodiversity for globally relevant evolutionary research.

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Conclusion

The Deccan Chronicle article brought attention to an important gap in India’s scientific ecosystem. While the situation reflects broader patterns seen across the Global South, it also highlights specific opportunities for growth. As discussions around curriculum design and scientific priorities continue, evolutionary biology may play a key role in shaping a more integrated understanding of life sciences — both in India and beyond.

Not So Hunky-Dory: When Genomes Look Like “Races” — And Why That’s Dangerous Thinking

The previous essay argued that the 1,000 Genomes Project and modern population genomics help dissolve the biological idea of race. That is broadly true — most human genetic variation is shared — but the story is more complicated. A number of solid, peer-reviewed studies identify loci where allele frequencies differ sharply between regions, sometimes to the point of near-fixation. Those differences matter for phenotype and health. Taken out of context, they can be (and historically have been) used to justify Morton-style claims of innate, hierarchical human difference.

Below I collect a handful of well-cited studies, include short quotes from the papers themselves, and then draw cautious, evidence-based inferences — and limits.

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1) Population structure exists — but it’s a matter of degrees

“Within-population differences among individuals account for 93 to 95% of genetic variation; differences among major groups constitute only 3 to 5%.” Science

Rosenberg et al. (2002) — a landmark study using hundreds of microsatellite markers across global samples — showed that most genetic variation is within populations, not between them. Still, the remaining 3–5% is enough for multilocus methods to cluster individuals by continent of origin. This is the factual basis for later claims that genetics can “recreate” population groups.

Why Morton-style readers seize on this: small but consistent allele-frequency differences across many loci let statistical methods assign an individual to a geographic cluster with high accuracy. That looks, superficially, like “biological races.”

Why that inference is weak: clustering arises from correlated allele-frequencies produced by population history (migration, drift, bottlenecks, isolation), not from discrete, immutable biological types.

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2) Multilocus classification works — Edwards’s critique of Lewontin

A.W.F. Edwards (2003) summarized the statistical point plainly: although most variation is within populations, “the correlation structure can be used to classify individuals into populations.” PubMed

Edwards’ point (often called “Lewontin’s fallacy”) is technical and important: looking locus-by-locus misses the information contained in correlations among loci. Put bluntly, many weak differences across many loci combined give strong predictive power.

What this enables: accurate ancestry inference and forensic/genetic clustering.
What this does not prove: any hierarchy of worth, intelligence, or moral capacity — those are extra-scientific claims.

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3) Single loci with large effect: pigmentation, lactase persistence, EDAR

Genetics gives us clear examples where a single allele explains a big, visible phenotype and has very different frequencies across regions.

• Skin pigmentation — SLC24A5: As Lamason et al. (and follow-ups) show, a single nonsynonymous SNP in SLC24A5 accounts for a large fraction of the European vs. West African skin-pigmentation difference. “A single nucleotide difference … accounts for 25–38% [of] European–African pigmentation differences.” ResearchGate+1

• Lactase persistence — LCT regulatory variants: Enattah et al. (2002) identified C/T-13910 upstream of LCT, which “completely associates” with lactase persistence in some European samples. This allele has high frequency in pastoralist populations and low frequency elsewhere. PubMed

• EDAR V370A — East Asian trait: The derived EDAR allele V370A shows a very high frequency in East Asians and affects hair thickness, tooth morphology, and sweat glands; it’s described as “one of the strongest candidates of recent positive selection.” PMC

Mortonish temptation: these are clear, functionally meaningful genetic differences that map onto continental regions — to a Morton-style reader, that looks like the kind of “fixed” biological difference he used to assert.

Reality check: these are specific, local adaptations tied to environment, diet, or demographic history. They do not imply global hierarchies of ability or worth.

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4) Near-fixations and sweeps: Duffy and malaria adaptations

Some alleles have essentially swept to fixation in particular regions because of strong local selection:

• The Duffy (FY*O) null allele rose to (near-)fixation in sub-Saharan Africa because it confers resistance to certain malaria parasites. Modern analyses reconstruct a strong selective sweep at this locus. PMC

• The HBB sickle-cell allele is geographically restricted and confers malaria resistance in heterozygotes — the classic example of balancing selection described by Allison. PubMed

Inference: local, high-impact selection can produce alleles at (or near) fixation in certain regions — so yes, at the level of single loci, you can get very strong, geographically stratified genetic differences.

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5) Putting it together: what the facts allow — and what they forbid

Facts

• Human populations differ in allele frequencies; multilocus patterns permit accurate geographic ancestry inference. Science+1

• Some single variants (e.g., SLC24A5, LCT regulatory variants, EDAR V370A, Duffy) show large frequency differences and clear phenotypic or physiological effects. PMC+3ResearchGate+3PubMed+3

What these facts do justify

• Scientific claims about population history, migration, and adaptation. (Why did EDAR rise in frequency? Why did lactase persistence evolve in pastoralists?) PMC+1

• Medical and public-health applications that use ancestry or variant frequency to predict risk or tailor treatment (pharmacogenomics, sickle-cell screening, G6PD deficiency considerations, etc.). Science

What these facts do not justify

• Any claim that allelic differences imply ranked moral, intellectual, or social worth. Genetics gives mechanistic explanations for phenotype, not blueprints for social hierarchy. Edwards’s statistical point about classification is not a biological justification for racism. PubMed

• The notion of discrete, bounded “races” with immutable essences — population structure is clinal, continuous, and shaped by history. Science

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6) The Mortonish danger: cherry-picking loci, reifying clusters

Morton’s error was not simply bad data; it was interpretive misuse: he treated a small set of measurements as if they proved a global, hierarchical natural order. Modern Mortonism would look the same: cherry-pick a few high-difference loci (skin pigmentation, EDAR, lactase, Duffy), and generalize from those to claims about intelligence, culture, or worth.

Two technical facts fuel that misuse:

1. Loci differ in effect size. A few loci explain big phenotypic differences (e.g., pigmentation, lactase). Generalizing those to global traits is unsound. ResearchGate+1

2. Multilocus clustering is predictive but not normative. You can predict ancestry; you cannot infer superiority from that prediction alone. Science+1

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7) Final caution: science’s power and limits

Yes — genomics gives us concrete examples of geographically stratified, functionally meaningful genetic differences. Those facts complicate a simplistic claim that “race is only a social construct” in the sense that some traits do track ancestry strongly. But acknowledging those facts does not bring us back to 19th-century biological hierarchy. Instead it imposes moral and scientific responsibilities:

• Do not conflate population-specific adaptation with global hierarchies of worth.

• Do use allele-frequency knowledge to improve medicine and understand human history.

• Do make social policy based on ethics, not on simplistic biological readings.

In short: the reality is not all hunky-dory — genetic structure and strong local adaptations exist and matter — but neither are these facts any justification for Morton-style racial hierarchies. The scientific task is to understand the mechanisms (selection, drift, migration), and the ethical task is to refuse any reduction of human dignity to allele counts.

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Key sources quoted or used above

• Rosenberg NA et al., Genetic structure of human populations (2002). Science

• Edwards AWF, Human genetic diversity: Lewontin's fallacy (2003). PubMed

• Lamason RL et al./Mallick et al., work on SLC24A5 and pigmentation. ResearchGate+1

• Kamberov YG et al., EDAR V370A selection and phenotypes (2013). PMC

• Enattah NS et al., LCT regulatory variant and lactase persistence (2002). PubMed

• McManus et al./others on Duffy (FY*O) sweep in Africa. PMC

• Allison AC, classic work on sickle-cell and malaria. PubMed