A high-stakes reading test as the White listening subject: Applying an antiracist validation lens
Bibliographic record
Abstract
In this paper, the White listening subject takes the form of a standardized high-stakes reading test, the State of Texas Assessment of Academic Readiness (STAAR). Although the test does not actually listen, it ‘hears’ and evaluates children’s responses to its questions. I present the results of the 2017 Grade 8 reading exams, from the March, May, and June administrations, with a focus on results for students who are learning English as an additional language, who are racially minoritized, and who are economically disadvantaged. The analysis looks at factors predicting test completion, passing rates, and test resitting: language proficiency status, race/racism, and economic disadvantage. In the discussion, I question these results’ validity by examining the STAAR validity arguments including the construct definition, development and scoring of the assessment, retake administration policies, and consequences for language minoritized and racialized students. I hope this study may spur changes in policy and practice, and the institution of a re-humanizing lens in assessment policies in Texas and beyond.
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 | 0.155 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".