Listening to the Voices of Rating Scale Developers: Identifying Salient Features for Second Language Performance Assessment
Bibliographic record
Abstract
This article describes the process and discourse stances of a team of teachers involved in deriving a rating scale for writing ability. The research was carried out within a Ministry of Education of Quebec (MEQ) project whose objective was to develop empirically based rating scales for secondary-level ESL provincial exams. The study focused on instances during the process where actions of the participants and/or their use of the data sample (i.e., student writing samples) could be shown to influence the criteria for the rating scale and in turn the final ratings (i.e., areas where there was potential for variation within the two test method characteristics of scale development team and sample used). Through a qualitative analysis, it expands on earlier research (Turner & Upshur, 1999) that reports on the quantitative results of method characteristics in such empirically derived scales. This study provides a description of the nature of these test method characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".