Best Practices for Hiring of Teaching Track Faculty Members
Bibliographic record
Abstract
The current hiring landscape for teaching faculty is a perfect storm, where two issues - (1) increasing demand but a lack of trained candidates and (2) highly diverse job expectations - combine to create a job search that can be overwhelming for teaching faculty candidates and disappointing for departments of computing. As departments independently navigate this new reality, they have each come up with their own practices for interviewing and screening teaching faculty candidates. This proliferation of hiring practices has made it increasingly difficult for teaching faculty candidates to identify positions that are a good fit and prepare adequately for the diversity of hiring practices. In response to these challenges, the Computing Research Association - Education committee surveyed faculty across Canada and the USA about their hiring practices. This work culminated in a recent white paper "Best Practices for Hiring Teaching Faculty in Research Computing Departments." Panelists will engage with the recommendations from this recent paper, discuss principles underlying some best practices for the hiring process, and field questions from the audience.
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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 | 0.090 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".