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Record W2934938577 · doi:10.7202/1056973ar

Evaluating the State of the Employment Relationship: A Balanced Scorecard Approach Built on Mackenzie King’s Model of an Industrial Relations System

2019· article· en· W2934938577 on OpenAlexaffvenueabout
Bruce E. Kaufman, Michael Barry, Rafael Gómez, Adrian Wilkinson

Bibliographic record

VenueRelations industrielles · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBalanced scorecardIndustrial relationsGovernment (linguistics)CommissionState (computer science)Public administrationPolitical scienceSociologyManagementComputer scienceLawEconomics

Abstract

fetched live from OpenAlex

The industrial relations (IR) field in Canada and the United States (US) emerged in the late 1910s-early 1920s and is thus on the cusp of its 100th anniversary. The impetus for the creation of the IR field was growing public alarm in both countries over the escalating level of conflict, violence, and class polarization in employer-employee relations. The two countries established federal-level government investigative committees, theRoyal Commission on Industrial Relations(1919) in Canada and theCommission on Industrial Relations(1911-1915) in the US, to travel cross-country, gather evidence, and report their findings and overall evaluation. To commemorate the IR field’s centenary, this paper conducts the same type of cross-national ER evaluation, but with modern methods. First, this exercise requires a formal evaluation instrument, like a physical exam worksheet. Adopted is a modified version of a balanced scorecard. Second, the scorecard’s framework and questions should be theoretically informed. The framework used is a modified version of the diagrammatic model of an IR system presented by Mackenzie King inIndustry and Humanity(1918). The third step is to fill in the scorecard with data from individual workplaces, which are obtained for the US from a new nationally-representative survey of 2000+ workplaces, theState of Workplace Employment Relations Survey(SWERS). The fourth step is to aggregate all the diagnostic measures to obtain a summary numerical estimate for each of the companies of its state of ER performance and health. Based on a 1-7 (7 = highest) scale, then converted to F to A grades, we find that the average ER grade given by managers is B+ and by employees C+. The company scores are graphed in a frequency distribution that visually represents, for the first time in the literature, the lowest-to-highest pattern of employment relations performance and health across the US.

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)0.037
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.072
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0150.013
Science and technology studies0.0020.007
Scholarly communication0.0090.011
Open science0.0010.005
Research integrity0.0010.002
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 head0.143
GPT teacher head0.344
Teacher spread0.200 · 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 designTheoretical or conceptual
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

Citations10
Published2019
Admission routes3
Has abstractyes

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