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Record W2601699015 · doi:10.1177/0019793917701116

The Impact of Training on Innovation

2017· article· en· W2601699015 on OpenAlex
Benoît Dostie

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

VenueIndustrial and Labor Relations Review · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsHuman capitalStock (firearms)ProductivityTraining (meteorology)BusinessProduct innovationLabour economicsIndustrial organizationMarketingEconomicsEngineeringEconomic growth

Abstract

fetched live from OpenAlex

The firm’s stock of human capital is an important determinant of its ability to innovate. As such, any increase in this stock through firm-sponsored training might lead to more innovation. The author tests this hypothesis using detailed data on firms’ human capital investments and innovation performance from the Canadian Workplace and Employee Survey, 1999–2006. The regression results, including workplace fixed effects and allowing for time-varying productivity shocks, demonstrate that more training leads to more product and process innovation, with on-the-job training playing a role that is as important as classroom training. Results from an event history analysis show, however, that this impact fades over time.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.168
GPT teacher head0.335
Teacher spread0.167 · 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