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What Is Best for Workers? The Implications of Workplace and Human Resource Management Practices Revisited

2010· article· en· W1492413702 on OpenAlexaffabout
John Godard

Bibliographic record

VenueIndustrial Relations A Journal of Economy and Society · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsSocial Sciences and Humanities Research CouncilUniversity of Manitoba
Fundersnot available
KeywordsHuman resource managementWork (physics)Perspective (graphical)Telephone surveyBureaucracyHuman resourcesRepresentation (politics)Quality of working lifeSurvey data collectionIndustrial relationsPublic relationsTrade unionBusinessJob satisfactionSociologyPolitical scienceLabour economicsManagementMarketingEconomics

Abstract

fetched live from OpenAlex

Drawing on a 2003–2004 random household telephone survey of 750 Canadian workers, I explore the implications of work and human resource (HR) practices for six aspects of the quality of working life. I find “traditional” HR practices, associated with the bureaucratic model predominant after World War II and with union representation, to have strong positive implications for workers. Participative workplace practices also have strong positive implications, although these are largely limited to information sharing in the union sector. The actual organization of work (e.g., teams), contingent pay, and “new” HR practices, associated with the “new” HRM of the 1980s, all make little difference. Comparison of these findings with those from a comparable 1998 survey of 508 Canadian workers and a parallel 2003 survey of 450 English workers suggest, however, that the implications of work and HR practices may be historically and institutionally contingent and thus should be interpreted using a historical/institutional perspective.

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)0.008
metaresearch head score (Gemma)0.020
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 score0.930
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0100.024
Scholarly communication0.0100.004
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.344
Teacher spread0.288 · 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

Citations74
Published2010
Admission routes2
Has abstractyes

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