MétaCan
Menu
Back to cohort
Record W2161795402 · doi:10.1002/nha3.20051

Human Performance Technology and HRD

2014· article· en· W2161795402 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

VenueNew Horizons in Adult Education and Human Resource Development · 2014
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsConcordia University
Fundersnot available
KeywordsHuman performance technologyKnowledge managementHuman resourcesProcess (computing)Empirical evidenceFoundation (evidence)Empirical researchOrganizational performancePsychologyElectronic performance support systemsComputer scienceEngineering ethicsManagementBusinessMarketingEngineeringPolitical scienceEpistemologyEconomics

Abstract

fetched live from OpenAlex

Performance—the achievement of results–is central to definitions of HRD. Performance Technology (HPT) refers to a systematic methodology for developing performance in individuals and organizations. Through a systematic process, HPT explores issues at the organizational, unit, and individual level, and with skills and knowledge, resources, and motivation. perceive several advantages of HPT: an explanation of why training alone often fails to achieve the intended results, a systematic approach, a scientific foundation and emphasis on empirical data, and the promise of more enriching jobs. reality is more nuanced: too much similarity between the HPT and instructional systems design models, a continued over–reliance on training; a lack of actual empirical evidence to support it; and its failure to actually provide more. continues to influence HRD, but perhaps HRD represents a more evolved approach.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.315
Teacher spread0.297 · 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