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Record W2790313418 · doi:10.1177/0022185617748990

Taking the pulse at work: An employment relations scorecard for Australia

2018· article· en· W2790313418 on OpenAlexaffabout
Adrian Wilkinson, Michael Barry, Rafael Gómez, Bruce E. Kaufman

Bibliographic record

VenueJournal of Industrial Relations · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndustrial relationsBalanced scorecardAuditWork (physics)Field (mathematics)BusinessPolitical scienceSociologyManagementAccountingEconomicsMarketingEngineering

Abstract

fetched live from OpenAlex

This study, and the project behind it, is an attempt 100 years on from the Webbs to comprehensively assess the health of the industrial relations/employment relations system by ‘taking the pulse’ of the employment relationship. If, as we argue, the relative health and performance of the employment relationship remains the key dependent variable of the field of employment relations today, there have been remarkably few attempts to audit and measure its critical dimensions. This study, founded on a large representative survey of workers and managers across Australia, the United States, the United Kingdom, and Canada, attempts to do just that, and produces in this article, results of those survey questions for Australia. The article is novel since this kind of employment diagnostic is based on a unique nationally representative survey of employers and employees. The study is also innovative, in that it presents the results of the health of the system in the form of an employment relations scorecard and is the first such attempt to do so in industrial relations.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.362
GPT teacher head0.479
Teacher spread0.117 · 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 teacher head, not a consensus.

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

Citations14
Published2018
Admission routes2
Has abstractyes

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