Prioritizing equitable social outcomes with and for diverse readers: A conceptual framework for the development and use of justice-based reading assessment
Bibliographic record
Abstract
Scholarship on the science of reading (SoR) has, in some instances, taken up more narrow views of reading in discussions and instantiations of reading assessment that do not center equity and justice, especially in schools. This can lead to less valid and even harmful reading assessment, especially for students from historically marginalized communities with diverse language, cultural, and neurological differences. Here, we draw on critically-minded reading research, as well as on work in equity-oriented educational assessment, to inform a justice-based reading assessment framework that can guide research, theory, policy, and practice. Using an equity-oriented and justice-based lens, the framework outlines three interwoven components: (1) relational and humanizing assessment practices; (2) justice-based products and outcomes; and, (3) a critical construct of reading. The framework compels designers, developers, and users to center the needs of rights-holders, and especially those from historically marginalized communities, throughout the assessment process. To do so, the framework outlines five principles that include orienting to equity and justice; prioritizing humanizing and critical assessment practices; grounding assessment in a complex, dynamic, and critical construct of reading for diverse populations; designing for justice-based social consequences, and engaging in critical debrief throughout. These principles guide eight phases of assessment, which we outline in detail. Finally, we discuss conceptual contributions as well as practical implications.
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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 | 0.083 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.004 |
| Science and technology studies | 0.010 | 0.088 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".