Rethinking Authority and Bias: Modifying the CRAAP Test to Promote Critical Thinking about Marginalized Information
Bibliographic record
Abstract
Many information evaluation methods include values like objectivity and authority that imply that only traditional scholarly sources are acceptable for inclusion in scholarly work. Although this is often a desirable outcome, it can bias research to exclude groups traditionally disenfranchised from scholarship, such as Indigenous, racialized, queer, and disabled communities. The CRAAP test, originally created in 2004, is a commonly taught method of source evaluation. The acronym, standing for Currency, Relevance, Authority, Accuracy, and Purpose, is intended to guide readers in thinking through different aspects of what makes a source trustworthy. Twenty years after its creation, increased efforts to include a diversity of perspectives have soured some of the CRAAP criteria. Its conception of authority and requirements that sources be unbiased, objective, and impartial risks excluding certain groups and people from scholarship. This article presents a few simple modifications to the CRAAP test that provide a means to evaluate marginalized information and prevent its exclusion.
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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 | 0.203 | 0.625 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".