Evaluating the State of the Employment Relationship: A Balanced Scorecard Approach Built on Mackenzie King’s Model of an Industrial Relations System
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".