Beginning teacher attrition: a question of identity making and identity shifting
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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