Privation of Inclusion: An Exploration of the Stealth and the Strategy that Sabotaged Racialized Public Servants’ Career Mobility in British Columbia, Canada
Bibliographic record
Abstract
This paper qualitatively examines “privation of inclusion” at work in the lived experiences of racialized participants hired in publicly funded places of employment. Taking the position that the dualistic inclusion-exclusion paradigm fails to capture their lived realities with inclusive exclusions and exclusive inclusions, it presents privative inclusion as a third space, between inclusion and exclusion, for a more robust framework in understanding how racialized bodies were marked and targeted for differential treatment. The paper then outlines and discusses findings as key indicators of privative mechanisms that had undermined life chances by limiting career mobility of racialized participants of this study. It concludes by emphasizing the need for additional research in this area given the salience of racism at work as well as the demographic changes that Canada is currently experiencing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.052 | 0.018 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".