Bibliographic record
Abstract
It is widely recognized that the public benefits from well-placed trust in science. While expert advice may be wrong at times, nonexperts, on balance, benefit from following scientific experts rather than ignoring them. In short, the public needs science. Numerous professional codes such as the 2017 European Code of Conduct for Research Integrity, scientific reports (e.g., American Association of Arts and Science. 2014. Public Trust in Vaccines: Defining a Research Agenda. https://www.amacad.org/sites/ default/files/publication/downloads/publicTrustVaccines.pdf) and academic scholarship emphasize the importance of public trust in science and recommend a variety of ways to promote it.Footnote1 Less attention, however, is given to the converse relation between science and the public, namely how much science needs the public. This article examines this two-way relationship by considering the role of trust in science, both within scientific communities and between science and the public, where and how public mistrust arises, and what can be done to improve public trust in science.
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 | 0.072 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".