A Framework for Using Consequential Validity Evidence in Evaluating Large-Scale Writing Assessments: A Canadian Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.098 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it