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Record W7132916449

Administrator Perceptions of Ontario's Teacher Performance Appraisal Process

2013· dissertation· en· W7132916449 on OpenAlexaffabout
Sachin Maharaj

Bibliographic record

VenueTSpace · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsCanadian Association for the Study of Adult Education
FundersOffice of International Science and Engineering
KeywordsPerformance appraisalPerceptionProcess (computing)Training (meteorology)Employee Performance Appraisal
DOInot available

Abstract

fetched live from OpenAlex

This study examines the views of administrators (i.e. principals and vice-principals) in Ontario, Canada with regards to the province’s Teacher Performance Appraisal process. A total of 178 responses were collected to a web-based survey that examined five areas: 1) Preparation and training; 2) Classroom observations; 3) Preparing the formal evaluation; 4) The impact on teaching practice; and 5) Improving the process. Results indicate that administrators did not receive extensive training and of the training they did receive, most did not find it very useful. Most administrators did not feel strongly that the classroom observations adequately assessed teacher practice and most did not feel that there had been substantial improvement in teacher practice in their schools as a result of the process. The most common suggestions for improvement were to have more classroom observations, some of which are unannounced; evaluate teachers more frequently; and have more than two rating categories.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.003
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.119
GPT teacher head0.487
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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