A Framework for Using Consequential Validity Evidence in Evaluating Large-Scale Writing Assessments: A Canadian Study
Bibliographic record
Abstract
The increasing diversity of students in contemporary classrooms and the concomitant increase in large-scale testing programs highlight the importance of developing writing assessment programs that are sensitive to the challenges of assessing diverse populations. To this end, this paper provides a framework for conducting consequential validity research on large-scale writing assessment programs. It illustrates this validity model through a series of instrumental case studies drawing on the research literature conducted on writing assessment programs in Canada. We derived the cases from a systematic review of the literature published between January 2000 and December 2012 that directly examined the consequences of large-scale writing assessment on writing instruction in Canadian schools. We also conducted a systematic review of the publicly available documentation published on Canadian provincial and territorial government websites that discussed the purposes and uses of their large-scale writing assessment programs. We argue that this model of constructing consequential validity research provides researchers, test developers, and test users with a clearer, more systematic approach to examining the effects of assessment on diverse populations of students. We also argue that this model will enable the development of stronger, more integrated validity arguments.
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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 | 0.571 | 0.662 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.052 | 0.035 |
| Science and technology studies | 0.026 | 0.061 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.013 | 0.024 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".