What Does It Mean to “Misuse” Research Data?
Bibliographic record
Abstract
ABSTRACT In this panel, we will discuss how “data misuse” is understood across different disciplines, and in particular digital curation, critical data studies, scholarly communication, and algorithmic fairness. The audience will be invited to contribute to the discussion by reporting on their own experience with data misuse, and brainstorming potential interventions to prevent misuse. Controversial reuses of open research data are emerging, including exploitation of marginalized communities, geo privacy violations, and perpetuation of harmful stereotypes. Incidents of data misuse hinder scientific progress and erode public trust, yet defining misuse remains challenging as one community's misuse might be another's best practice. The development of a shared framework to understand when, how, and why misuse of research data occurs can help science stakeholders decide when and how to release crucial research data, evaluate the potential for misuse, and tailor documentation of research data to prevent misuse. Our goal for this panel discussion is to take us a step closer to the development of such a theoretical framework for defining data misuse.
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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 | 0.023 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".