COLLABORATIVE RESEARCH, DELIBERATION, AND INNOVATION
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 I evaluate the extent to which we could learn something about how we should be conducting collaborative research in science from the research on groupthink. I argue that Solomon has set us in the wrong direction, failing to recognize that the consensus in scientific specialties is not the result of deliberation. But the attention to the structure of problem-solving that has emerged in the groupthink research conducted by psychologists can help us see when deliberation could lead to problems for a research team. I argue that whenever we need to generate alternative solutions or proposals, groupthink is a genuine threat, and research teams would be wise to allow individuals opportunities to work alone. But the benefits of team work emerge when scientists seek to evaluate the various proposals generated, and determine a course of action. Then the group is less prone is groupthink, and the interaction of group members can be an epistemic asset.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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