Bibliographic record
Abstract
This paper examines an inflammatory subject - `DNA typing' (or `DNA fingerprinting' as it is popularly called) - to show how credible scientific knowledge is produced through the systematic erasure of uncertainty and random variation. This erasure occurs at the levels of measurement and of group processes. I use the history of DNA typing in the United States as a case study to develop a three-fold argument. First, that objective knowledge is achieved through interactions and social processes that erase the actions of the representing subject from the representations made of the natural world. Second, that this set of social processes and interactions includes normative judgements, but that these judgements do not pose a threat to objectivity: instead, they are what constitute objectivity. Objectivity is attained through personal judgements and evaluations of what is good enough to constitute an objective measurement, or judgements of how big or small a deviation must be to count for or against a particular theoretical interpretation. Third, I argue that measurements, the result of the translation of properties of the natural world into numbers, always have a gap or error between the theoretically predicted measurement and the empirically obtained measurement. At some point this error cannot be reduced any further. The final measurement is the outcome of negotiation and interaction between people and nature. I demonstrate that these judgements, evaluations and negotiations take place within a laboratory, when forensic workers construct estimates of measurement error, and also in negotiations among disparate social groups involved in calculating random match probabilities. Scientific and legal disputes about the construction of measurements and measurement standards for DNA profiling began in the late 1980s, and continued for the better part of a decade. In this paper, the protracted controversy provides especially rich materials for examining the social construction of measurement.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.082 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".