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Transfer of Learning From An Action Reflection Learning™ Program*

2008· article· en· W2059646836 on OpenAlexaff
Lyle Yorks, Judy O’Neil, Victoria J. Marsick, Sharon Lamm, Robert C. Kolodny, Glenn E. Nilson

Bibliographic record

VenuePerformance Improvement Quarterly · 2008
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsLearning Partnership
Fundersnot available
KeywordsAction learningTransfer of learningReflection (computer programming)Action (physics)Human resourcesTransfer of trainingKnowledge managementExperiential learningProcess (computing)Formal learningField (mathematics)Computer scienceCooperative learningPsychologyMathematics educationManagementArtificial intelligenceTeaching method

Abstract

fetched live from OpenAlex

Learning transfer from training and development programs to the workplace is a central issue of concern for human resource development specialists. Action Reflection Learning™ (ARL™) is a form of action learning that places emphasis on the development of targeted competencies while teams of participants work on real problems which have been given to them by a sponsor. This article examines the kinds of learning and learning transfer that took place in an international food company that was striving to become a global organization. Data were collected through both field observation and formal interviews and analyzed by a team of researchers. Conclusions about the degree of transfer and the mechanisms and process supporting it are presented.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.073
GPT teacher head0.356
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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

Citations21
Published2008
Admission routes1
Has abstractyes

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