New Dawn or Bad Moon Rising? Large Scale Government Administered Workplace Surveys and the Future of Canadian IR Research
Bibliographic record
Abstract
This article discusses the potential advantages of large scale, government administered workplace surveys and the limitations of these surveys in the past. It then reviews the 1995 AWIRS (Australia), the 1998 WERS (U.K.), and the 1999 WES (Canada) in accordance with how well they appear to have succeeded in overcoming these limitations, and, more generally, with their implications for the conduct of industrial relations (IR) research. It is argued that the 1995 AWIRS does not appreciably overcome the limitations of previous surveys. In contrast, the 1998 WERS has yielded a substantially higher quality data set, although it also does not completely overcome the limitations of its predecessors. Finally, the 1999 WES promises an even higher quality data set, but is primarily a labour market and productivity survey rather than an IR survey, and could even portend a “bad moon rising” for Canadian IR research.
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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 | 0.102 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".