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Record W2153868268 · doi:10.1111/1467-6451.00168

Strategy Fads and Competitive Convergence: An Empirical Test for Herd Behavior in Prime‐Time Television Programming

2002· article· en· W2153868268 on OpenAlex
Robert E. Kennedy

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.

Bibliographic record

VenueJournal of Industrial Economics · 2002
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsWestern University
Fundersnot available
KeywordsImitationHerd behaviorConvergence (economics)Competition (biology)Prime (order theory)Test (biology)EconometricsVariable (mathematics)Computer scienceEconomicsMicroeconomicsHerdingMathematicsGeographyEcologyPsychology

Abstract

fetched live from OpenAlex

The economics literature contains many theoretical analyses of imitation and differentiation strategies but relatively few empirical studies of these topics. This paper aims to address that shortcoming. I analyze program introductions by television networks and then compare the payoffs to imitative and differentiated introductions. The analysis indicates that the networks imitate each other when introducing new programs and that, on average, imitative introductions underperform differentiated introductions. These results are consistent with theoretical models of herd behavior but are difficult to explain using standard models of spatial competition or the possibility of omitted variable bias.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.294
Teacher spread0.229 · 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