Expertise, Skepticism and Cynicism: Lessons from Science & Technology Studies
Bibliographic record
Abstract
The topic of expertise has become especially lively in recent years in academic discussions and debates about the politics of science. It is easy to understand why the topic holds such strong interest in Science & Technology Studies (STS) and related fields. There are at least two basic reasons for such interest. One is that experts are undoubtedly important in modern societies, and the other is that trends in STS research tend to be critical of the cognitive authority associated with the public role of the expert. Putting the two together, STS researchers often align themselves with environmentalist and other movements that question the impartiality of experts and seek to democratize decisions about science and technology. Though such alignment is in many respects laudable, it can also be a source of confusion and misplaced political criticism. Toward the end of this brief synopsis of current STS research and debates on the topic of expertise, I will suggest an alternative agenda for engaging the politics of science and technology.
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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".