How Do Scientific Views Change? Notes From an Extended Adversarial Collaboration
Bibliographic record
Abstract
There are few examples of an extended adversarial collaboration, in which investigators committed to different theoretical views collaborate to test opposing predictions. Whereas previous adversarial collaborations have produced single research articles, here, we share our experience in programmatic, extended adversarial collaboration involving three laboratories in different countries with different theoretical views regarding working memory, the limited information retained in mind, serving ongoing thought and action. We have focused on short-term memory retention of items (letters) during a distracting task (arithmetic), and effects of aging on these tasks. Over several years, we have conducted and published joint research with preregistered predictions, methods, and analysis plans, with replication of each study across two laboratories concurrently. We argue that, although an adversarial collaboration will not usually induce senior researchers to abandon favored theoretical views and adopt opposing views, it will necessitate varieties of their views that are more similar to one another, in that they must account for a growing, common corpus of evidence. This approach promotes understanding of others' views and presents to the field research findings accepted as valid by researchers with opposing interpretations. We illustrate this process with our own research experiences and make recommendations applicable to diverse scientific areas.
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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 | 0.183 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.072 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.012 | 0.039 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".