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Record W4384700286 · doi:10.1111/1744-7941.12337

Issue Information

2023· paratext· en· W4384700286 on OpenAlexaff
Lee-Shing Fang, Jillian Cavanagh, Hannah Meacham, Patricia Cabrera, Siah Hwee Ang, Leila Afshari, Hugh T. J. Bainbridge, Greg J. Bamber, John Benson, Alan Brown, Edith Cowan, Pawan Budhwar, Xuebing Cao, Wayne F. Cascio, Christina Cregan, Ali Dastmalchian, Helen De Cieri, Nikola Djurkovic, Peter J. Dowling, Lee Dyer, Tony Edwards, Shea X. Fan, Justine Ferrer, Kirsteen Grant, Patrick Gunnigle, Jarrod Haar, Beni Halvorsen, Charmine E. J. Härtel, Kate Hutchings, Boris Kabanoff, Sunghoon Kim, Yasuo Kuwahara, Russell D. Lansbury, Malcolm Rimmer, Neil Semuel Rupidara, Debi S. Saini, Randall S. Schüler, Jeff Shao, Ed Snape, Shlomo Y. Tarba, Louise Thornthwaite, Dean Tjosvold, Keith Townsend, Rosalie L. Tung, Simon Fraser, Patrick M. Wright, Yucheng Zhang, Shuming Zhao

Bibliographic record

VenueAsia Pacific Journal of Human Resources · 2023
Typeparatext
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCitationComputer scienceLibrary scienceInformation retrievalWorld Wide Web

Abstract

fetched live from OpenAlex

Asia Pacific Journal of Human Resources is an applied, peer-reviewed journal which aims to communicate the development and practice of the field of human resources within the Asia Pacific region. The journal publishes the results of research, theoretical and conceptual developments, and examples of current practice. The overall aim is to increase the understanding of the management of human resources in an organisational setting.

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)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.178
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.006
Science and technology studies0.0020.001
Scholarly communication0.0110.006
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.8220.682

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 head0.033
GPT teacher head0.339
Teacher spread0.306 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAsia Pacific Journal of Human ResourcesSame topicHuman Resource Development and Performance EvaluationFrench-language works237,207