The Four-Capital Theory as Framework for Teacher Retention and Attrition
Bibliographic record
Abstract
There has been a strong interest in teacher retention and attrition which has been studied extensively over the past twenty or more years. While some researchers have attributed teacher attrition to low teacher salaries, poor working conditions, lack of administrative support and resources, other research focuses on the “emotional” aspect of the profession where educators continue to stay because of their love for teaching, for their students, and how they imagine possibilities for their students’ futures. A more comprehensive theory of retention and attrition is Mason and Matas’ (2015) four capital framework which consists of human capital, social capital, structural capital, and positive psychological capital. In our research with teacher residents in a preparation program, we used interviews, survey, and focus group to obtain data, and found strong prevalence of the four capitals as competing and intersecting phenomenon aiding in understanding the varied and complex factors that contribute to teacher retention or attrition. Additionally, we found that one or more of these four capitals may significantly impact teacher retention or attrition more than others, at any given time and one type of capital may help to overcome limitations in another. Therefore, we found this to be a worthwhile framework to incorporate in teacher preparation.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".