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Record W3122839107

Conformism and Diversity Under Social Learning

2001· article· en· W3122839107 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEUR Research Repository (Erasmus University Rotterdam) · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsDiversity (politics)Social learningMicroeconomicsKnowledge managementSociologyEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

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...

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.001
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.245
GPT teacher head0.414
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it