Scientific Objectivity vs. Cultural Bias: How Science Reflects Society

TAKEAWAYS:

• Scientific "objectivity" is a cultural construct, not a neutral fact—even "hard" sciences embed societal values (e.g., Martin’s analysis of sperm/egg metaphors).
• Cultural bias in research appears in language (e.g., "aggressive" vs. "assertive"), methodology (e.g., Western-centric clinical trials), and interpretation (e.g., framing poverty as "laziness" vs. "systemic failure").
• Nepali examples show bias in data collection (e.g., NTC’s traffic reports ignoring pedestrian safety) and policy narratives (e.g., NEPSE’s "market efficiency" ignoring caste-based investment barriers).
• To critique texts, ask: Who funds the study? What metaphors does it use? Whose voices are missing? Use the "Bias Audit" framework (author, audience, assumptions, omissions).
• Ethical writing requires acknowledging limits (e.g., "This study reflects urban Kathmandu’s experiences") and centering marginalized perspectives (e.g., Dalit voices in health research).
• The scientific method itself is culturally contingent—e.g., the "double-blind" trial assumes neutrality, but placebo effects vary across cultures (e.g., Ayurveda vs. allopathy).

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1. What Is Scientific Objectivity? The Myth and the Reality

Scientific objectivity is often taught as the gold standard: facts untouched by human emotion or culture. But as philosopher Sandra Harding argues, "objectivity is always objectivity for someone." Here’s why:

The Illusion of Neutrality

• Definition: Objectivity in science means reproducibility, testability, and lack of personal bias—but this is an ideal, not a reality.
• How it works:
  1. Empirical evidence (data) is collected.
  2. Theories are built to explain patterns.
  3. Peer review (other scientists) checks for flaws.
  • Problem: Even peer review is shaped by dominant paradigms (e.g., psychology’s historical focus on white, male subjects).

Real-World Example: The "Scientific" Gender Binary

Why it matters: These metaphors aren’t just language—they influence real-world policies, like IVF ethics or sex education curricula.

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2. How Culture Shapes "Facts": Three Mechanisms

Science doesn’t operate in a vacuum. Cultural beliefs seep into research through:

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3. Case Study: Emily Martin’s The Egg and the Sperm

Martin’s 1991 essay exposes how biology textbooks reinforce gender stereotypes through metaphor and narrative. Here’s her breakdown:

Step-by-Step Analysis

1. Metaphor of War (Sperm)

   • Textbooks describe sperm as "swimming rapidly," "competing," "penetrating" the egg.
   • Bias: Frames reproduction as a male-driven conquest.
   • Real-world impact: Used to justify patriarchal control over family planning (e.g., "men decide contraception").

2. Metaphor of Home (Egg)

   • The egg is "large," "passive," "waiting" in a "protective" environment.
   • Bias: Reinforces the domestic sphere as female territory.
   • Real-world impact: Justifies women’s unpaid care work as "natural."

3. Scientific "Facts" as Cultural Artifacts

   • Martin shows that even cell biology is told as a romantic story:
     • "The sperm is the hero; the egg is the princess."
   • Key question: If we rewrote the story, would the "facts" change?
     • Example: Some cultures describe fertilization as a mutual dance, not a battle.

Worked Example: Kathmandu’s Traffic Narratives

• Official NTC reports: Traffic is "chaotic," caused by "irresponsible drivers."
  • Bias: Blames individuals, not lack of pedestrian infrastructure.
• Alternative framing: Traffic is a systemic failure—sidewalks are missing, buses are overcrowded, and police prioritize cars.
  • Data to support: 70% of road accidents in Kathmandu involve pedestrians (NTC 2022).
• Critical question: Whose safety does NTC’s language protect?

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4. Bias in Nepali Institutions: Three Local Examples

A. NEPSE (Nepal Stock Exchange) and "Market Efficiency"

• Claim: NEPSE’s annual reports describe the stock market as fair, transparent, and meritocratic.
• Bias:
  • Exclusion: Caste-based discrimination limits Dalit/Bahun access to brokerage firms.
  • Narrative: "Rags-to-riches" stories ignore inherited wealth (e.g., Newar trading families).
• Hidden assumption: Success is individual effort, not structural privilege.

B. NTC (Nepal Telecommunications) and Digital Divide

• Claim: NTC’s broadband expansion is neutral and accessible.
• Bias:
  • Urban bias: 80% of fiber-optic networks serve Kathmandu Valley; rural areas rely on slow 3G.
  • Language: Technical manuals use English terms (e.g., "bandwidth," "latency") without Nepali explanations, excluding non-elite users.
• Real-world impact: Rural students can’t access online TU exams.

C. Banks and "Creditworthiness" Metrics

• Claim: Banks like Nabil, Global IME assess loans based on credit scores.
• Bias:
  • Informal economy ignored: 80% of Nepal’s workforce is in agriculture—no formal records.
  • Gender bias: Women are 3x less likely to get loans, framed as "high-risk" (even with equal income).
• Alternative metric: Community collateral (used by Grameen Bank) centers social trust, not just financial data.

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5. How to Audit a Text for Bias: The 4-Step Framework

Use this Bias Audit Checklist to critique any scientific, policy, or academic text:

1. Author & Audience

   • Who wrote this? (e.g., a male neuroscientist vs. a Dalit feminist scholar).
   • Who is the intended reader? (e.g., policymakers vs. grassroots communities).
   • Example: A World Bank report on poverty will use economic jargon, while a Dalit rights NGO will focus on caste discrimination.

2. Language & Metaphors

   • Look for loaded words: "illegal migrants" vs. "undocumented workers."
   • Example: NTC’s "traffic violators" vs. Pathao’s "ride-hailing users"—who gets blamed?

3. Assumptions & Omissions

   • What’s taken for granted? (e.g., "Nepali culture values family"—but which families?).
   • Who’s missing? (e.g., LGBTQ+ voices in health surveys).
   • Example: TU’s social work syllabus historically ignored disability studies until 2020.

4. Evidence & Methods

   • Is the data representative? (e.g., Kathmandu-centric vs. federated).
   • Are alternative methods possible? (e.g., qualitative interviews vs. just surveys).
   • Example: NEPSE’s quantitative growth reports hide qualitative stories of small investors losing savings in 2008 crash.

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6. Can Science Be Truly Objective? The Debate

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7. Ethical Writing: Centering Marginalized Perspectives

To write fairly across disciplines (especially social work), follow these principles:

1. Acknowledge Your Positionality
   • Example: "As a Kathmandu-based researcher, my analysis may overlook rural Magar communities’ experiences."
2. Use Inclusive Language
   • Avoid: "The mentally ill," "third-world countries."
   • Use: "People with disabilities," "Global South."
3. Cite Diverse Sources
   • Bad: Only Western psychology journals.
   • Good: Include Nepali studies (e.g., Central Department of Sociology, TU).
4. Challenge "Neutral" Narratives
   • Example: Instead of "Kathmandu’s traffic is bad," ask: "Whose mobility is prioritized?"

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In the Real World

1. eSewa & Digital Exclusion

   • Idea: Algorithmic bias in eSewa’s loan approval system.
   • How: The app’s risk model penalizes users with no formal job (e.g., farmers, daily wage workers), assuming they’re "unreliable."
   • Real impact: 60% of rural applicants are rejected—reinforcing urban bias.

2. Khalti’s "Cashless Nepal" Narrative

   • Idea: Cultural assumption that all Nepalis have smartphones.
   • How: Khalti’s marketing ignores elderly users (30% of Nepal) and low-income groups who can’t afford data.
   • Alternative: USSD-based payments (like M-Pesa in Kenya) would include more people.

3. Pathao’s Driver vs. Passenger Framing

   • Idea: Power dynamics in language.
   • How: Pathao’s app calls drivers "partners" but passengers "customers"—who holds the power?
   • Critique: The one-star rating system gives passengers unfair control over drivers’ livelihoods.

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Exam Tip

How This Unit Is Tested (Based on Past Questions)

1. Case Study Analysis (30% of marks)
   • Example: "Analyze how Emily Martin’s essay critiques scientific objectivity."
   • Your answer must:
     • Quote specific metaphors (e.g., "sperm as warrior").
     • Link to real-world impact (e.g., IVF ethics, gender roles).
     • Use 1 academic source (e.g., Harding’s The Science Question in Feminism).

2. Bias Audit Task (25% of marks)

   • Example: "Audit this NTC traffic report for cultural bias."
   • Structure:

     1. **Author**: Government agency → likely prioritizes **car users** over pedestrians.
     2. **Language**: "Traffic violators" → **blames individuals**, not **road design**.
     3. **Omissions**: No data on **pedestrian fatalities** in rural areas.
     4. **Assumptions**: "Nepalis are irresponsible" → ignores **lack of infrastructure**.
     

3. Ethical Writing Application (20% of marks)

   • Example: "Rewrite this bank loan policy to reduce bias."
   • Do:
     • Replace "high-risk applicants" → "applicants with non-traditional income sources."
     • Add: "We recognize that informal economies contribute 80% of Nepal’s GDP."
   • Avoid: Generic statements like "We are fair"—show fairness with data.

4. Debate Question (15% of marks)

   • Example: "Do you believe a ‘pure’ fact exists?"
   • Strong answer:
     • Agree: Cite reproducible experiments (e.g., gravity).
     • But: Add critical caveat: "Even ‘pure’ facts are interpreted through cultural lenses (e.g., Newtonian physics vs. quantum theory)."
     • Link to social work: "If we can’t have pure objectivity, we must center marginalized voices to reduce harm."

Pro Tip: Always tie theory to Nepal. Examiners love answers that connect Emily Martin’s sperm/egg to NEPSE’s investor narratives or NTC’s traffic reports.

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Final Visual Summary