How the public, and scientists, perceive advancement of knowledge from conflicting study results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Science often advances through disagreement among scientists and the studies they produce. For members of the public, however, conflicting results from scientific studies may trigger a sense of uncertainty that in turn leads to a feeling that nothing new has been learned from those studies. In several scenario studies, participants read about pairs of highly similar scientific studies with results that either agreed or disagreed, and were asked, “When we take the results of these two studies together, do we now know more, less, or the same as we did before about (the study topic)?” We find that over half of participants do not feel that “we know more” as the result of the two new studies when the second study fails to replicate the first. When the two study results strongly conflict (e.g., one finds a positive and the other a negative association between two variables), a non-trivial proportion of participants actually say that “we know less” than we did before. Such a sentiment arguably violates normative principles of statistical and scientific inference positing that new study findings can never reduce our level of knowledge (and that only completely uninformative studies can leave our level of knowledge unchanged). Drawing attention to possible moderating variables, or to sample size considerations, did not influence people’s perceptions of knowledge advancement. Scientist members of the American Academy of Arts and Sciences, when presented with the same scenarios, were less inclined to say that nothing new is learned from conflicting study results.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it