Editor’s Introduction: The “Accidental California Issue” – Critical Questions about Fairness and Equity in Writing Assessment and Placement
Bibliographic record
Abstract
JWA 17.2 features five articles that explore these evolving practices and critical questions around fairness and equity. Daniel Gross (2024) examines the implications of construct validity in the discontinuation of the Analytical Writing Placement Examination (AWPE) at the University of California. Julia Voss, Loring Pfeiffer, and Nicole Branch (2024) share how they used interviews from programmatic assessment to understand student learning outcomes in ways that value minoritized students’ experiential knowledge. Edward Comstock (2024) investigates the interplay between self-efficacy and programmatic assessment, emphasizing the value of qualitative methods in evaluating writing programs. Sarah Hirsch, Kenneth Smith, and Madeleine Sorapure (2024) present on Collaborative Writing Placement (CWP). Julie Prebel and Justin Li (2024) critique of a first-year writing portfolio assessment through lenses of equity, curricular design, performance, and reliability.
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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 | 0.008 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".