Bibliographic record
Abstract
In collective bargaining, General Wage Increases (GWI) are most normally framed and implemented as percentages, with each eligible member seeing a salary rise of X% on top of pre-existing salary. While this approach is not remarkable where salary grids are in place and union members start at the same rate, it can have significant effects where starting salaries vary, as is common in the university sector. Under these conditions, percentage increases over time contribute to the widening of intra-member salary inequity, exacerbating structurally gendered and racialized inequities of the academic labour market. This paper explores the impact of a flat rate increase approach to salary bargaining. Beginning with the context of collective bargaining in British Columbia, it examines how percentage-based and flat-rate increases would impact real salaries of faculty members at Simon Fraser University in order to better understand how faculty associations and unions could use flat rate approaches to begin to counteract the impact of differential starting salaries on the career earnings of faculty members. The paper finds that flat rate increases could be an effective tool against pay inequity even where that inequity is driven by forces outside the university.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".