Questioning the Research on Early Career Teacher Attrition and Retention
Bibliographic record
Abstract
In this paper, we consider scholarly work on early career teacher attrition, and retention, from 1999 to 2010. Much of the literature has framed attrition as either a problem associated with individual factors (e.g., burnout), or a problem associated with contextual factors (e.g., support and salary). Some recent conceptualizations consider early career teacher attrition as an identity- making process that involves a complex negotiation between individual and contextual factors. On the basis of our review, we suggest the need to shift the conversation from one focused only on retaining teachers, toward a conversation about sustaining teachers. This shift offers the possibility of new insights about teacher education and about the kinds of spaces needed on school landscapes to sustain and retain beginning teachers. Cet article porte sur les travaux académiques évoquant l'attrition et la rétention des enseignants en début de carrière entre 1999 et 2010. Une part importante de la littérature présente l'attrition comme un problème associé à des facteurs individuels (par ex. épuisement professionnel) ou bien à des facteurs contextuels (par ex. appui et salaire). Selon certaines conceptualisations récentes, l'attrition d'enseignants en début de carrière serait un processus de formation identitaire impliquant des négociations complexes entre l'individu et des facteurs contextuels. À partir de notre analyse, nous évoquons le besoin de s'éloigner des conversations portant exclusivement sur le besoin de retenir les enseignants pour discuter plutôt de soutien aux enseignants. D'un tel changement peuvent découler de nouvelles idées sur la formation des enseignants et le type de milieux scolaires nécessaires pour appuyer et retenir les enseignants en début de carrière.
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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 | 0.036 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".