Luke Ford

Given that anything that violates one’s hero system is experienced as harm, the category of “harm” governing academic research must be rife with abuse that advances one hero system over another.

Mike McIntire writes for the New York Times:

Genetic researchers were seeking children for an ambitious, federally funded project to track brain development — a study that they told families could yield invaluable discoveries about DNA’s impact on behavior and disease.

They also promised that the children’s sensitive data would be closely guarded in the decade-long study, which got underway in 2015. Promotional materials included a cartoon of a Black child saying it felt good knowing that “scientists are taking steps to keep my information safe.”

The scientists did not keep it safe.

A group of fringe researchers thwarted safeguards at the National Institutes of Health and gained access to data from thousands of children. The researchers have used it to produce at least 16 papers purporting to find biological evidence for differences in intelligence between races, ranking ethnicities by I.Q. scores and suggesting Black people earn less because they are not very smart…

Within weeks of Mr. Trump taking office, the N.I.H. made a little-noticed revision to the guidelines on access to a large genetic database. Its description of what constituted stigmatizing research no longer included any reference to skin color, ancestry and ethnicity.

When a research finding suggests that a core tenet of that system—such as the idea that all human outcomes are purely a product of effort or environment—is false, it is not processed as a simple data point. It is experienced as an assault on the “heroic” narrative that gives the individual’s life and society its moral order.

In this context, “harm” becomes a flexible and potent tool for exclusionary closure. If the NIH defines harm not as physical injury, but as the “disruption of social cohesion” or the “promotion of negative stereotypes,” they have essentially turned a subjective psychological reaction into a regulatory barrier.

The shift in research ethics from “protection against physical risk” (the Belmont Report model) to “protection against dignitary or social harm” allows institutions to pick a winning hero system.

The Individualist/Hereditarian Hero System: This system values the “truth at all costs” and the mastery of nature through data. It views the suppression of data as the ultimate harm (a “Galilean” harm).

The Egalitarian/Environmentalist Hero System: This system values social equity and the protection of vulnerable groups. It views research that suggests innate inequality as a “weaponized” harm that justifies systemic cruelty.

By codifying “harm” in their guidelines, the NIH is not acting as a neutral arbiter of facts. They are acting as the curator of the national hero system. They determine which “truths” are productive and which are “dangerous.”

The abuse of the “harm” category usually occurs through semantic expansion. In the ABCD study, the “harm” cited by Dr. Jernigan was that the science was “faulty” and “unethical.” However, in science, “faulty” is usually handled by peer review and counter-studies, not by banning the researcher and ordering the destruction of their copies of the data.

When the state (via the NIH) decides that certain conclusions are “evil,” they are doing exactly what you suggest: advancing one hero system by legally pathologizing the other.

The NIH treats these two frameworks with a stark asymmetry. Research that falls under the hereditarian risk category, such as the suppression of uncomfortable data, is usually dismissed as a standard scientific debate. The institution views the stagnation of knowledge or a lack of balanced policy as a minor cognitive harm compared to the alternative.

In contrast, the NIH categorizes environmentalist risks as significant social harms. If a study reinforces stereotypes or provides a justification for discrimination, the agency labels the work as misconduct or stigmatizing research. This leads to an institutional response involving legal sanctions and the removal of data access.

The current system essentially creates a one-way guardrail. Research that leads toward environmental conclusions remains pro-social and aligns with the heroic narrative of a perfectible society. Work that suggests biological limits is labeled anti-heroic because it threatens the institution’s fundamental goal of solving problems through social engineering.

Because the current elite hero system is built on the “blank slate” or environmentalist model, research that leaps to environmental conclusions—even if those conclusions are based on thin data—is seen as “pro-social.” It reinforces the heroic narrative that society can be perfected through the right interventions.

Conversely, research that suggests biological limits or innate differences is “anti-heroic.” It suggests that some problems may not be solvable through social engineering. In a bureaucracy dedicated to social improvement, the latter isn’t just a different perspective; it is a threat to the institution’s reason for being.

Status closure refers to the process where a dominant group restricts access to resources or legitimacy to maintain its own prestige and power. When you apply this to the NIH, you are describing a “gatekeeping” mechanism where the agency uses its control over massive datasets (the resource) to ensure only those who adhere to specific ethical or “mainstream” frameworks (the circle of eligibles) can participate.

The NIH essentially uses three mechanisms to enforce this closure against what it deems “unapproved” or “stigmatizing” research:

Credentialism: To access the ABCD data, you cannot just be a citizen scientist or a freelance researcher. You must be a tenure-track professor or a senior scientist at a recognized institution. This automatically excludes “outsiders” and ensures the NIH has institutional leverage (the threat of firing or loss of grant money) over the researcher.

The Data Use Certification (DUC): This is the legal “boundary work.” It isn’t just a set of rules; it is a contract that defines what constitutes “responsible” science. By including clauses against “stigmatizing research,” the NIH reserves the right to define what is a “scientific fact” versus “ideological harm.”

Methodological Monopolization: The NIH frequently argues that because race is a social construct and not a biological one, any study using the ABCD data to rank races is methodologically flawed by definition. By defining the correct way to do science as one that ignores or minimizes group-level genetic differences, they effectively close the door on hereditarian perspectives before a paper is even written.

Sociologists like Frank Parkin, who developed closure theory, would point out that the NIH is using exclusionary closure to protect the “sacralized” status of certain social groups.

The NIH treats environmentalist perspectives as the approved and default stance for research. This approach faces no restrictions and is actively encouraged through prompts and guidelines in all data applications. In contrast, the agency views hereditarianism as a stigmatized or fringe perspective. Researchers pursuing this angle must undergo strict responsible use training and face the threat of potential life-long bans from federal repositories.

The most severe category is race science, which the NIH categorizes as strictly prohibited. Engaging in this type of work triggers legal sanctions, formal orders to destroy compromised data, and institutional firing. By categorizing these perspectives in this manner, the agency ensures that only research fitting the preferred social framework receives the legitimacy of federal data.

Because the current elite hero system is built on the “blank slate” or environmentalist model, research that leaps to environmental conclusions—even if those conclusions are based on thin data—is seen as “pro-social.” It reinforces the heroic narrative that society can be perfected through the right interventions.

Conversely, research that suggests biological limits or innate differences is “anti-heroic.” It suggests that some problems may not be solvable through social engineering. In a bureaucracy dedicated to social improvement, the latter isn’t just a different perspective; it is a threat to the institution’s reason for being.

There are no binding guidelines from the NIH that penalize researchers for ignoring genetic data to focus solely on social or environmental factors. While the NIH recently updated its “Responsible Use” warnings for the ABCD Study to urge researchers to “account for social and environmental contexts,” it does not mandate the reverse.

This asymmetry stems from the specific ethical and legal frameworks that govern federal research data.

The NIH guidelines are focused on preventing informational risk and social harm. Under federal policy, “stigmatizing research” refers specifically to work that could promote negative stereotypes or lead to the marginalization of specific groups.

Genetic Leaps: When a researcher uses genetic data to suggest innate group differences, the NIH views this as a high risk for social detriment. This triggers the strict oversight seen in the Pesta case.

Environmental Leaps: When a researcher ignores genetics to focus on environment, the NIH generally sees this as a low-risk “scientific disagreement” rather than an ethical violation. Even if the research is considered incomplete by some, it does not carry the same historical baggage or potential for systemic harm as eugenics-related research.

While the NIH encourages “transdisciplinary research” that combines genetics and social science, the enforcement mechanisms are very different for each side:

For Geneticists: To even touch the data, they must sign a Data Use Certification (DUC). This is a binding legal document. If they use the data in a way that promotes “disrespectful reporting of disaggregated data” (such as race-based rankings), they can be banned for life, as Pesta was.

For Social Scientists: They are guided by “Responsible Use” callout boxes and checklists. These are suggestions for rigor rather than binding contracts. A social scientist who ignores a relevant genetic marker might have their paper criticized by peers for poor methodology, but they will not face federal sanctions or lose their access to the database.

The NIH maintains that its role is not to settle the “nature vs. nurture” debate, but to ensure that the data the public pays for is used responsibly. In their view, “responsible” means avoiding the weaponization of DNA. Because the social science tradition of focusing on the environment is not seen as “weaponizable” in a way that leads to group-based discrimination, it remains a “safe” path in the eyes of federal regulators.

Essentially, the NIH has built a one-way guardrail: it stops you from driving off the “genetic determinism” cliff, but it allows you to stay on the “environmental determinism” road as long as you like.

Social science and genetics often exist in a state of mutual suspicion. Most social science research operates on the assumption that environment explains nearly all variation in life outcomes. This creates a vacuum that researchers like Pesta and Kirkegaard seek to fill. They argue that because mainstream science ignores heritability, their work provides a necessary correction.

The friction lies in how one defines “heritable.” In behavioral genetics, the consensus holds that most complex traits, including intelligence, possess a significant genetic component. However, the “hereditarian model” used by the researchers in the story goes a step further by claiming that differences between groups are primarily genetic.

The researchers in the story did not just focus on genetics; they used a specific type of data called a Polygenic Score. This score aggregates thousands of tiny genetic variants to predict a trait.

The technical failure in their work, according to critics, is not that they looked at DNA, but how they accounted for the “noise” in that DNA:

Genetic Stratification: Genetic markers often cluster by geography and ancestry without having anything to do with the trait being studied. If a researcher does not properly control for these clusters, they might mistake a marker for “ancestry” as a marker for “intelligence.”

The Scarcity of Environmental Data: The ABCD Study and the Philadelphia data were designed to track brain development, not sociology. They lacked granular data on wealth, lead exposure, nutrition, and early childhood stress. By using the genetic data in isolation, the researchers essentially assigned all “unexplained” variation to DNA.

The raw data points—the IQ scores and the genetic sequences—were real. The children in the study did provide those samples and take those tests. The “lie” or “truth” depends on the inference.

If a child in a high-poverty neighborhood has a lower test score and a specific genetic marker, a social scientist might say the score is due to the poverty. A hereditarian might say it is due to the marker. Because the researchers lacked the data to prove the environment wasn’t the cause, mainstream scientists argue their “accurate” data was used to reach an unsupported conclusion. They essentially looked at a race where some people ran in sand and others ran on a track, then claimed the speed difference was entirely due to the runners’ lung capacity.

The question of “ideological guidelines” versus “scientific standards” is the central tension in this story. From the perspective of the NIH, these guidelines exist to prevent what they call “stigmatizing research”—studies that use federal data to promote negative stereotypes or social harm. Critics like the researchers in your story see this as a form of “cancel culture” or an ideological “no-fly zone” that prevents them from asking certain questions.

The NIH justifies these restrictions on two grounds: informed consent and scientific rigor.

Informed Consent: The families who joined the ABCD study in 2015 were promised their data would be used to help children, not to provide fodder for social media posts about racial inferiority. The NIH argues that allowing “race science” violates the fundamental agreement made with the participants.

Preventing Pseudoscience: The agency discourages research that uses “social constructs” (like race) as proxies for “biological predictors” (like specific genes). They argue that because “race” is a social category with huge environmental variables (poverty, education, discrimination), treating it as a pure biological variable is scientifically illiterate.

The “bad guys” in this Times narrative—Bryan Pesta and his collaborators—were found to have used deceptive tactics to get the data, but the accuracy of their findings is where the real debate lies.

Mainstream geneticists call this “playacting at science.” They argue the researchers didn’t “find” evidence; they tortured the data until it confessed to their existing beliefs. By ignoring the massive environmental gaps between different groups in America, the researchers essentially attributed social inequality to DNA.

The most direct lie wasn’t about the IQ scores themselves, but about the source of those scores. The researchers claimed the data showed innate differences, but because they failed to control for the vastly different lives those children lead, their “discovery” was effectively a measurement of American sociology, not human biology, according to the dominant narrative.

In the view of many independent creators, the mainstream media (MSM) functions as a centralized gatekeeper that maintains “elite oversight” by filtering information through established institutional and corporate lenses. The rise of bloggers, vloggers, and podcasters represents a decentralized counter-movement that seeks to bypass these traditional “gates.”

Mainstream institutions often operate under a “propaganda model,” where information is filtered through several layers before reaching the public. These filters are not necessarily conspiratorial; they are often the result of organizational routines and institutional norms.

Institutional Alignment: Large media firms are often part of corporate conglomerates. This creates a structural incentive to avoid topics that challenge the interests of parent companies or major advertisers.

Access Journalism: Mainstream reporters often rely on high-level government or corporate sources. This dependency can lead to “regulatory capture,” where the media is hesitant to criticize the very sources they need for “scoops.”

Narrative Homogeneity: Professional norms (such as “objectivity” or “standardized fact-checking”) can sometimes lead to a “view from nowhere” that ignores marginalized perspectives or alternative data that doesn’t fit the established “hero system.”

Independent creators use a different model—often called “gatewatching” or “collaborative gatekeeping”—to challenge these centralized narratives.

Disintermediation: Podcasters and bloggers remove the “middleman.” They speak directly to their audience, allowing for long-form, unedited discussions that bypass the 5-minute televised soundbite.

Niche Expertise: Independent creators often specialize in highly specific fields (e.g., specific scientific data, local government, or specialized tech) that mainstream outlets might find too complex or “fringe” to cover consistently.

Audience Accountability: While independent creators lack the legal and financial resources of the MSM, they are often more directly accountable to their “community.” If a creator loses the trust of their base, they lose their funding (e.g., through Patreon or Substack) immediately.

As independent media has grown, mainstream institutions have responded by emphasizing their own role as the sole “authenticators” of reality.

The “Misinformation” Label: Mainstream outlets frequently frame independent media as a source of “misinformation” or “fake news.” By defining independent voices as “unverified,” they reinforce their own status as the only “responsible” gatekeepers.

Platform Pressure: Many elite institutions push for social media platforms to “de-rank” or “shadowban” independent voices that step outside the “approved” guidelines. This is often framed as a way to protect the public from “harmful” content.

By bypassing elite oversight, independent creators are effectively creating an alternative public sphere. This sphere doesn’t just offer different facts; it offers a different “hero system” where the individual’s ability to parse data is valued over the institution’s authority to declare what is true.

The battle between hereditarian researchers and the scientific establishment illustrates the The Neutralization Theory of Hatred by transforming a data-driven conflict into a struggle for social survival. In this framework, the establishment views hereditarian research as a “toxic” threat that imposes a “net fitness cost” on society by undermining the current “hero system” of egalitarianism and social perfectibility.

The establishment uses exclusionary closure to neutralize these researchers, treating their existence within the academic community as a cost that must be mitigated. Access to the ABCD Study data is restricted to researchers with “valid institutional credentials,” effectively barring “citizen scientists” or freelance researchers who might challenge the prevailing narrative.

Because the NIH discourages “stigmatizing research,” hereditarian researchers like Bryan Pesta often feel forced into deceptive applications. This deception then provides the establishment with a non-ideological justification—”misconduct”—to neutralize them through career-ending bans and institutional firing.

By defining race as a purely social construct rather than a biological variable, the NIH creates a “no-fly zone”. Any researcher attempting to use the data otherwise is labeled “unscientific,” effectively depriving them of allies and social power.

There is a direct contradiction between White House mandates and NIH practices. While the White House Office of Science and Technology Policy (OSTP) has mandated that federally funded research and data be “freely and immediately available” by 2026, the implementation remains stratified.

The “open source” mandate primarily targets peer-reviewed publications. However, the underlying “supporting data”—especially sensitive human genomic data like that in the ABCD Study—remains locked behind “controlled access” tiers.

The NIH maintains that data must be shared in a “responsible” manner. This “responsibility” includes protecting marginalized groups from “stigmatization,” a category that is expanded to justify blocking research that contradicts the dominant ideological agenda.

As the establishment attempts to neutralize these researchers, the researchers engage in their own information warfare. They bypass mainstream gates by using vlogs, blogs, and social media to appeal directly to a counter-public. This creates a “snowball effect” of reciprocal hatred, where any defense of the “toxic” individual is seen as a cost, leading the establishment to “hate the defender” as well.

The establishment’s claim that data is being “twisted to advance an ideological agenda” is mirrored by the researchers’ claim that data is being “sequestered to protect an ideological agenda.” Both sides are using science to protect their respective hero systems.

Alliance Theory suggests that the clash between researchers and the NIH is not a debate over abstract scientific values like objectivity or safety, but a social conflict driven by shifting alliance structures.

The theory argues that partisans use “propagandistic biases”—victim, perpetrator, and attributional biases—to support their allies and oppose their rivals.

Perpetrator Biases and “Misconduct”: The NIH’s focus on Pesta’s “misconduct” can be viewed as an outward-facing tactic to mobilize opposition to a rival. By framing the issue as a violation of safety protocols rather than a suppression of data, the institution downplays its own role in restricting research while maximizing the severity of the researcher’s transgression.

Victim Biases: The New York Times story highlights the grievances of families who felt “misused”. Alliance Theory suggests these victim biases serve a strategic function: they call attention to critical disadvantages to mobilize support from third parties and justify the neutralization of the researchers.

Status Closure as Alliance Strategy

The paper reframes the concept of “status closure” by showing that people do not simply cheer for ideologies; they rally for or against distinct ethnic, religious, and occupational groups.

Strange Bedfellows: The NIH represents a “bridging alliance” between intellectual elites (academics, journalists, scientists) and the groups they view as allies (vulnerable minorities).

Neutrality as a Signal: In this framework, “scientific consensus” is not a deep-seated moral value but a collection of ad hoc justifications designed to advance the interests of this complex alliance against its rivals.

The “Truth” vs. The “Ally”

The subject story notes that data is “twisted to advance an ideological agenda.” This paper explains why: ideological worldviews are not designed to literally view the world but to serve strategic functions like signaling allegiance.

Consistency is a Myth: The paper notes that political belief systems are collections of rationalizations and moralizations. The NIH’s “open science” mandate for peer-reviewed papers—while maintaining “controlled access” for raw data—is an inconsistency that serves the strategic function of maintaining oversight while appearing transparent.

Loyalty as Accuracy: For the partisans involved, motivated reasoning is not a cognitive shortcoming; it is an “honest signal of loyalty” to their side of the story.

Ultimately, the paper suggests that the primary difference between the NIH establishment and the rebel researchers is not a difference in values, but a difference in whom they view as their allies.

The concept of no-fly zones of inquiry, which relates to the sacralization of certain groups, explains why the academic focus remains asymmetrical. In this framework, certain topics or conclusions become socially and professionally off-limits because they threaten the status or perceived safety of groups that society has deemed protected or sacralized.

Academics often operate within a set of unspoken boundaries where questioning the agency or cultural habits of a minority group is viewed as an act of harm. Because these groups are sacralized, any data that might reflect poorly on them is frequently redirected toward an external cause, usually the dominant group. This creates a protective canopy over the minority group. To suggest that a problem originates from within that community violates the social taboo. It risks the researcher being labeled as a bigot or an agent of oppression.

By contrast, the dominant group—in this case, whites—is not sacralized in the same way within modern elite epistemics. Criticizing the dominant group or assigning them collective blame is not seen as an act of harm, but as an act of justice or “punching up.” This creates a dynamic where one side of the equation is open for rigorous, often harsh investigation, while the other side is protected by a no-fly zone. Researchers find it much safer for their careers to investigate the “sins” of the powerful than to explore the “failures” of the marginalized.

This asymmetry shapes the entire body of available research. When certain questions are never asked because they fall within a no-fly zone, the resulting lack of data is then used as evidence that those factors do not exist. If no one receives funding or tenure for studying the internal cultural drivers of crime in a specific neighborhood, the academic record will eventually only contain papers about the external structural drivers. The “truth” of the matter becomes a reflection of what was allowed to be studied.

The result is a closed loop. The no-fly zone prevents the inquiry; the lack of inquiry prevents the data; and the lack of data reinforces the consensus that the external, dominant group is the only variable that matters. This illustrates David Pinsof’s point that argument is often a tool used to hurt enemies and bolster allies. In this case, the academic framework serves to protect the sacralized ally and pressure the non-sacralized opponent.

Academics and public health researchers generally address high rates of STDs among gay men through the framework of Minority Stress Theory. This approach shifts the focus away from individual or group “blame” and toward the psychological and social toll of living in a heteronormative or exclusionary society.

The primary academic explanation suggests that chronic stress resulting from stigma, prejudice, and discrimination leads to higher rates of substance use and risky sexual behavior as coping mechanisms. Scholars argue that when a group is marginalized, they often lack access to supportive social institutions, which can lead to a breakdown in community health norms. In this view, the “root cause” of the disparity is not the behavior itself, but the societal hostility that drives it.

Structural factors also play a significant role in the academic literature. Researchers point to “medical mistrust” and unequal access to healthcare. They argue that gay men may avoid regular testing or preventative care like PrEP due to past negative experiences with healthcare providers or a fear of being judged. By focusing on these systemic barriers, academics can address the high rates without implying that there is an inherent or cultural problem within the gay community. This keeps the inquiry within the “safe” zone of institutional critique.

When applying the concept of no-fly zones of inquiry, a clear pattern emerges. Academics are often hesitant to investigate internal cultural drivers, such as the “sexual marketplace” dynamics or the impact of high partner turnover within certain subcultures, unless those factors can be linked back to external oppression. To suggest that the high rates might be partially driven by group-specific cultural norms or individual choices—independent of “white” or “straight” influence—often violates the protective canopy surrounding a sacralized group.

In this context, the “bad researchers” mentioned in the genetics debate are those who might look for biological or deep-seated behavioral predispositions. Mainstream academics view such research as a path toward pathologizing a marginalized group. Instead, they prioritize “resilience” frameworks and “upstream” social determinants. This ensures that the responsibility for the health disparity remains on the broader society and its institutions, rather than on the community experiencing the health crisis.

This leads to a research landscape where a great deal is written about “homophobia as a public health crisis,” but very little is written about the internal community norms that might contribute to the same statistics. The no-fly zone ensures that the sacralized group is viewed primarily as a victim of external forces, which maintains the epistemic moral high ground for the researchers and the group itself.