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Record W4389804946 · doi:10.1002/cjas.1741

Abandonment issues: A hazard analysis of high‐performance work practices

2023· article· en· W4389804946 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsAbandonment (legal)Work (physics)HazardHuman resourcesProcess (computing)Measure (data warehouse)Event (particle physics)Human resource managementProcess managementOperations managementBusinessComputer scienceKnowledge managementManagementPolitical scienceEngineeringEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This study examines usage of high‐performance work practices (HPWPs) over time using a unique longitudinal survey of Canadian organizations. Human resource management (HRM) studies tell us HPWPs require long‐term commitment but rarely measure it. Using event history analysis this study finds high rates of HPWP abandonment. The study also examines organizational supports for high‐performance work system (HPWS). A Cox regression analysis finds rates of abandonment are reduced when HPWPs are accompanied by aligned business strategies and HR professional support. The results inform process research on HRM strategies and raise troubling questions for findings in studies that fail to measure duration. For managers, the findings highlight the importance of ensuring strategic alignment with and organizational support for HPWPs if they are to endure.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.009
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.080
GPT teacher head0.308
Teacher spread0.228 · 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