Excavating the Labour Dispute Data from Statistics Canada: A Research Note
Bibliographic record
Abstract
Has worker militancy changed as a result of restructuring and globalization? Have the gender-specific impacts of the 'new economy' politicized women workers in particular, especially those in the public sector? These two research questions are guiding this project.The first step has been to seek out the available Canadian statistical data which might help map worker militancy. This research note introduces the reader to the ways that Statistics Canada surveys handle militancy. The Labour Force Survey [LFS] and the Survey of Labour and Income Dynamics [SLID], in which the proxy for the larger concept of 'worker militancy' is 'labour dispute' will help make visible the demographic characteristics of the striker. The Workplace and Employee Survey [WES] differentiates 'work-to-rule, work slowdown, strikes, lockouts and other labour related actions' and will offer a profile of the striker in the worker questionnaire, and a profile of the firm in the employer questionnaire.Although the statistical tables have not yet arrived, the design of the surveys provides an interesting window into Statistics Canada's perceptions about unions and their relevance. In fact, the process of excavating these surveys reveals absences that are intensely ideological and ways of posing questions that prevent the significance of union activity from emerging.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.091 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".