EXPLORING SCHOOL PRINCIPALS’ HIRING DECISIONS: FITTING IN AND GETTING HIRED
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
Hiring preferences can often determine the amount and kind of consideration shown to candidates for teaching positions, and therefore can have a profound impact on school culture, but have been largely unexplored. This paper describes how one group of principals in Manitoba approach hiring decisions when assessing prospective teachers for “fit” both for the profession and for their schools. Based on a conceptual framework that examined the criteria used in hiring decisions along four sub-categories of person-environment (P-E) fit (Kristof-Brown, Zimmerman, & Johnson, 2005), the findings illustrate the critical role that principals can play in assessing applicants along various dimensions of fit even though they may have little formal preparation that would increase the reliability of such assessments. Additionally, these highly interpretive assessments constitute a significant part in decisions of who to hire, even though little is known about the relationship between assessments of fit and teacher effectiveness in the classroom. Finally, suggestions are offered that might improve the likelihood that those responsible for hiring teachers are aware of some of the biases that influence various decision-making phases of the hiring process.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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 it