An Integrated Design and Appraisal Framework for Ethical Writing Assessment
Bibliographic record
Abstract
In my introduction to this special issue, I highlighted the insufficiency of key measurement concepts--fairness, validity, and reliability--in guiding the design and implementation of writing assessments. I proposed that the concept of ethics provides a more complete framework for guiding assessment design and use. This article advances the philosophical foundation for our theory of ethics articulated by Elliot (this issue). Starting with fairness as first principle, it examines how safety and risk can be addressed through the application of an integrated design and appraisal framework (IDAF) for writing assessment tools. The paper is structured around two case studies set in Alberta, Canada. Case Study 1 applies Kane's (2013) IUA model of validation to an appraisal--Alberta's English 30-1 (grade 12 academic English) diploma exam program--highlighting in the process the limitations in contemporary validity theory. Case Study 2 examines an assessment design project I am currently undertaking in partnership with 8 English language arts teachers in southern Alberta. This case study examines how the IDAF supports ethical assessment design and appraisal.
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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 | 0.459 | 0.299 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".