Attrition of certified teachers in secondary education during the induction phase
Bibliographic record
Abstract
Teacher attrition is generally considered problematically high, with attrition rates of beginning teachers up to 50%. This study shows the problem is not as big as has been claimed before. Previous studies have often focused on the attrition within 5 years, showing a quarter or more of beginning teachers leaving the profession. However, this disregards the fact that teachers leave at later stages as well, and the fact that some beginning teachers are not qualified to work as a teacher. Using administrative data from payroll administrations of schools in the Netherlands for secondary education a reliable measure of teacher attrition was made. Administrative data on diplomas in higher education were used to establish if teachers are certified at the start of their career. The results of this study show that the attrition rate of beginning teachers is only high within the first year of their career. The attrition rate within 1 year of experience is around 12% until the early 2000s, rising to close to 20% in more recent years. In comparison with other countries this seems relatively modest. However, after the first year, a base rate of attrition (retirement excluded) remains fairly constant at approximately 3% to 5% every year, explaining the gap with high attrition rates found in earlier studies. Attrition of certified teachers within one year is about 9%, with very little variation over time, versus the 12% to 20% of all beginning teachers. This 9% attrition rate of certified teachers is much lower than many earlier studies suggest.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".