MétaCan
Menu
Back to cohort
Record W3183400880 · doi:10.1086/719277

The Dynamics and Spillovers of Management Interventions: Evidence from the Training within Industry Program

2022· article· en· W3183400880 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.

Bibliographic record

VenueJournal of Political Economy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicItaly: Economic History and Contemporary Issues
Canadian institutionsKellogg's (Canada)
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of California, San DiegoBooth School of Business, University of ChicagoErasmus Universiteit RotterdamPontifícia Universidade Católica do Rio de JaneiroKU LeuvenLeonard N. Stern School of Business, New York UniversityYale University
KeywordsSpillover effectTraining (meteorology)BusinessPsychological interventionGovernment (linguistics)Production (economics)Panel dataIndustrial organizationEconomicsMicroeconomicsPsychologyEconometrics

Abstract

fetched live from OpenAlex

This paper examines the long-term and spillover effects of management interventions on firm performance. Under the Training Within Industry (TWI) program, the U.S. government provided management training to firms involved in war production between 1940 and 1945. Using a newly collected panel dataset on all 11,575 U.S. firms that applied to the program, we find that the TWI training had positive and long-lasting effects on firm performance and the adoption of beneficial managerial practices. Moreover, it generated complementarities among different types of training and had positive spillover effects on the supply chain of trained firms.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.080
GPT teacher head0.278
Teacher spread0.198 · 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