Affirming the abilities of new teachers: A teacher induction project within the Golden Horseshoe Learning Consortium
Bibliographic record
Abstract
This paper presents a partnership among Brock University’s Faculty of Education, the Ontario College of Teachers and six school boards that resulted in a teacher induction project conceptualized to enable new teachers to self-affirm their professional capacities as teacher leaders. The new teacher participants from 6 district school boards in southern Ontario were engaged in critical thinking and retrospective analysis with experienced teacher mentors from these same school boards. The outcomes of the project suggest that the induction model validated new teachers ’ meaningful contributions to their students and schools and heightened their sense of self-empowerment as reflective and active teacher leaders. Conceptual Framework Teacher induction is in fact a current and relevant educational initiative across North America. Darling-Hammond (2006) states that teachers ’ abilities directly impact upon student achievement and educational improvement (see, also, Cochran-Smith, 2006). Effective teacher induction programs are crucial in terms of retaining teachers and
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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 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".