Hiring Practices and its Connection to the Conceptualization of ‘a Good Teacher’ in Diverse Classrooms
Bibliographic record
Abstract
Abstract: This article explores the role of hiring practices in the conceptualization of what makes a teacher ‘a good teacher’, especially in diverse classrooms. It is based on a multiple case study that examined the perceptions of the experience of four high school English language arts educators teaching in diverse classrooms in Alberta, Canada. These practitioners suggest that hiring practices are mostly unreliable as principals’ hiring decisions are often strongly influenced by noticeable teachers’ personal traits and their preconceived perceptions of what a ‘good’ teacher is. During the hiring process, such interpretation often relies on the sole perceptions of the experience of school principals, which are usually not supported by effective hiring practices research. Hiring ‘the most suitable’ teacher to meet the needs of increasingly complex classrooms is key to education. As education strives for responding to the needs of an increasingly diverse community of learners, reflecting on hiring practices is important to improve the quality of the education provided to students. Teachers’ perspectives on hiring practices are important as they could potentially contribute and inform professional development initiatives, teacher preparation programs, and educational policies regarding a possible connection between administrative roles and teacher effectiveness.
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.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.051 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".