Nutrigenomics, Popular Representations and the Reification of "Race"?
Bibliographic record
Abstract
I. Introduction The concept of has long been controversial. Not only is it a profoundly socially divisive notion, its relevance to biomedical research community remains contested. Few in scientific community would claim that centuries old, socially constructed categories of race--such as black, white, Asian--have true biological significance. The human species cannot be categorized along clear biological demarcations. Indeed, we humans are a remarkable genetically homogenous lot. And more we learn about subtle genetic variations that make each of us biologically unique, more it seems that social concept of is a biological fiction. (1) That said, there are, no doubt, subtle and identifiable genetic differences between discrete populations based on geographic origin. Understanding these differences can facilitate genetic research and, ultimately, disease treatment and prevention. And an exploration of difference does not lead, inextricably, to social dilemmas. As noted by one scientist: Depending on how we use this information, potential exists to describe simultaneously our similarities and differences without reaffirming old prejudices. (2) But it is easy to slip from a discussion of genetic variation between populations to use of biologically crude and politically and historically complex notion of race. As such, researchers, clinicians and entities that provide genetic services to public must be careful how they communicate genetic information. In this paper, I explore concept of race in context of emerging field of It has been said that the assumption of real genetic markers that distinguish one ethnic group from another is at philosophical heart of nutrigenomics. (3) Given this perspective, might marketing of nutrigenomic services and products facilitate re-legitimization of race as a biological concept? (4) II. Nutrigenomics, Genetics and Race The current value of nutrigenomic testing has been questioned by many, including popular press, (5) some in scientific community, (6) government agencies, (7) and non-governmental organizations. Despite this apprehension, some companies already market nutrigenomic tests directly to public and more will likely follow. (8) The business strategy for these companies varies, but many already offer nutrigenomic testing to public. As suggested on one company's website, aim of testing is to provide personalized health and nutrition recommendations based on an individual's diet, lifestyle and unique genetic profile. (9) The website for this company goes on to suggest that nutrigenomic testing will help public develop a gene-based road map to health. (10) As part of marketing of nutrigenomic testing, it seems likely or even inevitable that race will be used, either explicitly or implicitly, as a marketing tool. (11) The scientific literature that surrounds nutrigenomics often refers to populations from Africa, Asia, and Europe. (12) At least one genetic testing company, DNA Direct, already advertises on their website for testing based on ethnic risk. (13) This is because genetic variations that may cause individuals to metabolize food differently can roughly correspond to broad social categories of race. Most Northern Europeans, for instance, can drink milk while many from Southeast Asian cannot. (14) This kind of geographically based variation can be found in other areas of genetic research, such as pharmacogenomics. (15) Indeed, many of emerging large-scale population studies are specifically designed to identify gene variations within and between sub-populations. (16) It is hoped that this research will lead to an understanding of how members of certain sub-populations may have genetic characteristics relevant to health; be it a predisposition to certain diseases, capacity to respond more effectively to a certain pharmaceutical, or ability to metabolize caffeine in a particular manner. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".