Negotiated Success: Contractual Benefits that Enhance Recruitment and Retention
Bibliographic record
Abstract
This report summarizes monetary and non-monetary items used in Collectively Bargained Agreements (CBAs) to enhance retention and recruitment of educators in and outside of Alaska. This report is one of a series commissioned by the Alaska Department of Education and Early Development to support a stakeholder-informed action plan to address the state’s recurring critical challenges in recruiting and retaining teachers. To approach this task, we narrowed our focus to a review of provisions contained within CBAs in Alaska and a sample of districts in the nation, reviewed relevant literature, and collected stakeholder feedback to further inform the report content and organization. We restrict our analysis to the information contained in CBAs, which are negotiated at the district level, with the noted limitation that CBAs are not exhaustive of all educator benefits (e.g., retirement is an important benefit that is managed at the statewide level). The report details benefits in five broad categories and 15 subcategories, which are bookmarked in this abstract for easy access: coming and staying (signing bonus, longevity/retention bonus); benefits – health and wellbeing (healthcare, sick leave, other leave); benefits – moving and living (travel and relocation, housing and utilities, childcare); knowledge and growth (transferable experience, education and certification, professional development); and work life (contract length and workday, extra duties, hard-to-staff areas, performance pay). Overall, we find that benefits and compensation vary significantly across districts in Alaska, and even more substantially across districts in the national sample, reflecting the diversity in the sample in terms of state, region, size, and location.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".