Teacher Retention is not the Solution to Teacher Attrition Problems
Bibliographic record
Abstract
Numerous studies discussed causes of high teacher attrition rates and provided solutions for improvement. Those studies often argued that the most troubling result of high teacher attrition was the decline in the quality of education. Although the negative factors that contribute to high teacher attrition rates should be addressed to improve teacher job satisfaction, I focus on how the quality of education may remain stable or even improve in the midst of high attrition rates. My case study explored factors that contributed to Canadian teacher attrition and retention; and, concluded that the main contributing factor was teacher personality. However, it would be erroneous to believe that the teachers who left simply could not handle the job. Indeed, those who left after a few years of K-12 teaching presented evidence during their interviews of having had provided high quality accessible education; and, many naturally moved up to other opportunities within the field of education. Therefore, I argue the focus needs to shift from teacher retention to effective teacher selection because it is better to have teachers who leave every few years but who are still able to provide high quality accessible education during their short time as K-12 teachers.
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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 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".