Bibliographic record
Abstract
Science today maintains an extremely high social profile. It is not only regarded as the purest and most ideal form of knowledge, it also acts as a paradigm for disinterested, objective and proven knowledge, incontestably providing our most secure sources of truths about the world. This may explain why feminists have had a good deal of difficulty and often reluctance in demonstrating the complicity of the natural sciences in the oppression of women. If, as science claims, it is true, observer-neutral, unbiased, repeatable and thus ‘objective’, it is difficult to see how it could contribute to or participate in women’s oppression. Many feminists working within the sciences can accept that the various social apparatuses, institutions and practices surrounding science—the funding and administration of scientific projects, the hiring of staff, practical applications of science, the development of technologies, etc.—are bound up with social values and power relations; but some consider it more difficult to criticise science itself. 'Pure science’ is usually considered immune to these sociological/political ‘issues’. It is hardly surprising, then, that the topic ‘women and science’ has generally focused on issues like ‘Great Women Scientists’, the Marie Curies of science, that is, on those individuals within science who have made ‘Great Discoveries’ and who also happen to be women. While this kind of analysis is important in raising consciousness about the difficulties women face in undertaking careers in the natural sciences, it only addresses the most superficial level of the problem. In this paper, we examine some deeper, structural investments and patriarchal commitments in science, including their implicit presumptions, their preferred, validated methods (methodological claims) and the criteria by which sciences are evaluated (epistemic claims).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.027 | 0.083 |
| Scholarly communication | 0.067 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".