Multiple Perspectives on Teaching Excellence: a Case Study
Bibliographic record
Abstract
Teacher compensation is the largest part of the monies spent by school boards in Ontario annually and currently all teachers are paid following a grid system that rewards teachers for time spent in the system rather than the quality of their work. For example, in 2018-19, the York Region District School Board’s instructional costs were 59% of their total budget at over 800 million dollars. An exemplary teacher costs the province and its school boards just as much as a poor one. Consider the possibility that teachers could be paid on a merit based model. There is currently no way to differentiate between poor and exemplary (or any other part of the spectrum) teachers. This research attempted to utilize an existing set of characteristics of excellence (Grieve 2010) to prepare a foundation upon which a methodology could be built to standardize a method to identify exemplary educators. The goal of this study was to understand the long-term perceived effect that George Turcotte has had on his students. Mr. Turcotte has been teaching elementary school in Kingston since 1966 and has taught in the public, the separate, and private systems over those years. He is widely acknowledged locally as an excellent teacher if not entirely unique. Any attempt to create a methodology of measuring excellence would need to include unique educators such as Mr. Turcotte.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".