Beginning teacher attrition: a question of identity making and identity shifting
Bibliographic record
Abstract
While there is discrepancy about the actual percentage of early career teachers that leave teaching in their first five years, one consistent discovery in a number of countries is that attrition is high for early career teachers. I became curious about early career teacher attrition as I watched colleagues leave the profession that they thought was a lifelong calling. In order to inquire into this phenomenon, I moved through a three-stage research process. First, I engaged in writing a series of stories about my experiences as a beginning teacher. Using autobiographical narrative inquiry, I then inquired into the stories in order to retell them looking for resonances across the stories. Secondly, I conducted a review of the literature, analyzing the studies to identify how the problem of early career teacher attrition was conceptualized. I identified two dominant problem frames: a problem frame situated within the individual and a problem frame situated in the context. Lastly, I offered a different conceptualization of the phenomenon of early career teacher attrition that draws on my autobiographical narrative inquiry and the literature review. I frame the problem of teacher attrition, not as a personal or a contextual problem frame, but as a problem of teacher identity making and identity shifting.
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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 | 0.043 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.044 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".