Bibliographic record
Abstract
There is in the science and values literature a core set of arguments that reject the value-free ideal at the inferential core of scientific investigation. They are usefully summarized in Kevin Elliott’s Values in Science (2022) under the following headings: 1) the gap argument; 2) the error argument; 3) the aims argument; and 4) the conceptual argument. I examine each of these arguments from a ‘meta’ perspective, wherein the arguments are turned on themselves. This is a possibility since each of these arguments is partially based various historical case studies that exemplify, for ‘values in science’ philosophers, scientific reasoning. This meta-investigation has a surprising result, that proponents of the value-ladenness of science are committed to a form of the value-free ideal in terms of the assumptions each of the above four arguments are required to make. A defense of the value-free ideal precipitates from this situation.
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 | 0.062 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.015 | 0.034 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".