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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".