AN EQUITABLE RECRUITMENT AND HIRING PROCESS IN THE ACADEMY?
Bibliographic record
Abstract
This paper describes the process of faculty recruitment and hiring in the Faculty of Education at Queen's University at Kingston (Ontario). The paper describes briefly, 13 features of an equitable search process, including identifying the unit's needs, goals, and personnel gaps; determining the criteria for the position; determining the interview questions; conducting the interviews; selecting a candidate for the position; and orienting the incumbent to the unit. How these steps are applied is then recounted in some detail for the recruitment and hiring of four tenure-track faculty. Also provided are examples of feedback on the process from the faculty at large, the newly hired faculty members, and the search committee. Among summary reflections are that each step must be carried out in a fair manner and be communicated to all stakeholders, that open and continuous communication within the faculty is as important as communication with applicants, and that striving for equity is difficult and takes continuous
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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 | 0.216 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.029 | 0.029 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".