Support for Beginning Teachers: An Invitation to Participate in a Collaborative Induction Process
Bibliographic record
Abstract
There is certainly no diminution of interest in teacher induction in the province of Ontario, Canada. Education Minister Gerard Kennedy has launched a concerted effort to implement the New Teacher Induction Program (NTIP) consisting of contextually relevant professional development opportunities for beginning teachers, an experienced teacher to serve as mentor, and school and district orientation sessions. The Minister has also proposed legislation that will streamline beginning teacher evaluation and formally credit participants for their successful completion of the program to be recorded on the Certificate of Qualification issued by the Ontario College of Teachers. The recently released NTI P Program Guideline (March, 2006) states that induction programs "will build on the faculty year experience by providing another full year of professional support” through the partnership among beginning teachers, mentors, principals, superintendents of the NTIP" etc. (pp. 3 & 4). The above measures underpin the Ministry’s initiatives to work collaboratively with education stakeholders and operationalize more pragmatic and efficient professional development initiatives to improve teacher induction practices in order to improve student learning .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".