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Record W2026380114 · doi:10.1111/1464-0597.00054

The Reciprocal Transfer of Learning from Journals to Practice

2001· article· en· W2026380114 on OpenAlexaff
Gary P. Latham

Bibliographic record

VenueApplied Psychology · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement Theory and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReciprocalPsychologySelection (genetic algorithm)Transfer of learningApplied psychologySocial psychologyComputer scienceDevelopmental psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

A strong case can be made for the reciprocal transfer of learning between the journals and practice, as well as for blurring the distinction between the science and practice of I/O psychology. This is because journals consistently provide answers for practice, and the implementation of the answers provides a basis for publication and subsequent research. The author’s research and practice in the areas of employee motivation, performance appraisal, training, and personnel selection are used in support of these arguments.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.095
metaresearch head score (Gemma)0.386
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.386
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.005
Science and technology studies0.0030.019
Scholarly communication0.0200.016
Open science0.0040.028
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0130.004

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 head0.037
GPT teacher head0.323
Teacher spread0.285 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations30
Published2001
Admission routes1
Has abstractyes

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