Developing Critical Information Literacy Pedagogies in the Face of Scholarly Misconduct
Bibliographic record
Abstract
ABSTRACT Drawing on the need to shift from competency‐based information literacy instruction to critical information literacy instruction, this study examines scholarly communication and scholarly misconduct in the health sciences based on literary evidence. We report on a qualitative systematic review of forms of scholarly misconduct in the health sciences and participants' attitudes towards these phenomena, with the goal of developing new, critical, approaches to information literacy instruction . The data synthesis process indicates there are four major areas of misconduct in health science research: in conducting research, publishing, following research protocols and determining authority. This categorization informs a framework for critical information literacy dispositions and pedagogies for researchers across health disciplines and geographies.
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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 | 0.111 | 0.286 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".