The ethical turn in writing assessment: How far have we come, and where do we still need to go?
Bibliographic record
Abstract
Both of us were drawn into the writing assessment field initially through our lived experiences as schoolteachers. We worked in radically different contexts – Martin was head of a languages department and teacher of French and German in the late 1990s in the UK, and David was a Grade 12 teacher of Academic English in Alberta, Canada, at the turn of the twenty-first century. In both these contexts, the traditional direct test of writing – referred to, for example, as the ‘timed impromptu writing test’ (Weigle, 2002, p. 59) or the ‘snapshot approach’ (Hamp-Lyons & Kroll, 1997, p. 18) – featured significantly in our practices, albeit in very different ways. This form of writing assessment still holds considerable sway across the globe. For us, however, it provoked early questions and concerns around the consequential and ethical aspects of writing assessment.
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 | 0.110 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.071 |
| Scholarly communication | 0.034 | 0.055 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.016 | 0.048 |
| Insufficient payload (model declined to judge) | 0.004 | 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".