Investigating Elementary Teachers' Perceptions About and Experiences with Ontario's Teacher Performance Appraisal System
Bibliographic record
Abstract
Performance appraisals have far reaching consequences on people. If evaluators in any way discriminate against employees, these individuals can suffer devastating and potentially debilitating consequences. This thesis investigates elementary teachers’ perceptions of and experiences with Ontario’s Teacher Performance Appraisal system (TPA), used to appraise teachers in Ontario from 2001 until 2007. I used quantitative data obtained from a sample of 132 teachers to investigate their perceptions of TPA with respect to four dimensions of organizational justice; outcome fairness, procedural fairness, informational fairness, and interpersonal fairness. Using oppression and critical theories as the theoretical framework, my analyses of my data allowed me to compare mainstream and minoritized teachers’ perceptions of their experiences with TPA. I also conducted follow-up interviews with three mainstream and three minoritized teachers. Analyses of my data enabled me to investigate how each group experienced TPA in terms of the four dimensions of organizational fairness. Analyses of the quantitative data revealed that minoritized teachers perceived their experiences less favourably than mainstream teachers. In addition, from my analyses of the qualitative data, I found that minoritized teachers tended to experience mistreatment, including manifestations of racism and homophobia from the administrators who conducted their TPA. The implications of this study call on administrators to disrupt the cycle of oppression by thinking about the biases, prejudices and stereotypical attitudes they bring intentionally or unintentionally to appraising teachers.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".