UNCOVERING BIAS IN JOB DESCRIPTIONS IN A PUBLIC SECTOR ORGANIZATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis examines the presence and impact of biased language in job descriptions within a public sector organization—the City of Saskatoon. Rooted in a human rights framework and guided by Critical Race Theory, discourse theory, and intersectionality, the research explores how recruitment language can serve as a mechanism of exclusion. Using a mixed-methods approach, the study analyzed 1,563 job descriptions through both content analysis and reflexive thematic analysis. Findings reveal systemic patterns of bias across dimensions such as age, race and ethnicity, gender, ability, education, and language. These biases often manifest through rigid criteria, culturally specific language, and limited recognition of diverse abilities and pathways. The study also introduces the miyo-wîcêhtowin Hiring Framework, an Indigenous-centered tool for fostering equity in hiring practices. The research contributes to the discourse on equitable employment by offering a replicable framework for assessing and addressing exclusionary language in recruitment, with implications for public sector policy and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it