Bibliographic record
Abstract
Th ere are many reasons why scientifi c experts may mask disagreement and endorse a position publicly as "jointly accepted."In this paper I consider the inner workings of a group of scientists charged with deciding not only a technically diffi cult issue, but also a matter of social and political importance: the maximum acceptable dose of radiation.I focus on how, in this real world situation, concerns with credibility, authority, and expertise shaped the process by which this group negotiated the competing virtues of reaching consensus versus reporting accurately the nature and degree of disagreement among them.I am concerned here with the epistemological asymmetry between scientifi c experts and the lay public, and the ways in which expert reporting contributes to that asymmetry.I will pay particular attention to the ways in which simplifi cation and especially "joint acceptance" result in the withholding of information from the public-especially information about the degree of disagreement among experts.To illustrate and motivate my points, I will discuss an example from the history of science: the deliberations, in the cold war era, of a group of scientists charged with assessing the genetic hazards of radiation exposure and determining what should count as a permissible dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.207 | 0.466 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".