Bibliographic record
Abstract
When there are competing technologies or products with unknown payo#s which are adopted over time within a society, an important question is whether conformism or diversity will prevail. We use a learning model with local interactions to study this question. We show that the structure of information #ows within a society helps to determine whether conformism or diversity obtains. We #nd that if information is public then society conforms to a single technology in the long run. On the other hand, if society consists of smaller groups of individuals and interaction within groups is more intense as compared to interaction across the groups, then two technologies can coexist and diversity obtains, in the long run. Our analysis involves a novel application of the Law of the Iterated Logarithm. Key Words: Social learning, local interactions, conformism#diversity, networks. JEL Classi#cation: D83, L15, O30, Q16, R10. 1 Dept. of Economics, McGill University, Montreal, and Econometric Insti...
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".