Effects of a formal mentoring program on teacher retention and benefits to proteges and mentors
Bibliographic record
Abstract
This thesis is an evaluation research study into the effects of a formal mentoring program \non teacher retention and the benefits to mentors and prot?g?s. The program studied took \nplace in the Keewatin-Patricia District School Board in Northwest Ontario during the \n1999-2000 school year, and involved the collection and subsequent examination of \nexperiential data collected from participants in the program. The respondents included \nexperienced teachers who served as mentors and new or beginning teachers who were the \nprot?g?s. A review of the literature outlined the benefits to mentors and prot?g?s as well \nilluminated such issues as mentor selection and training and descriptions of several other \nmentoring programs. Further, characteristics of mentor teachers are discussed. The data \ncollected are coded into categories based on benefits to mentors from a personal as well as \na professional viewpoint. Benefits to prot?g?s are discussed in terms of qualities of mentor \nteachers, technical support provided, and communication between mentors and prot?g?s. \nTeacher retention is defined by school board statistics relating to the number of beginning \nteachers who made the decision to continue teaching with the school board the following \nyear.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".