Envisioning Ethical Frameworks for Community-Data
Bibliographic record
Abstract
Emerging funder and journal requirements for research data management impact community-engaged research. In Canada, the Tri-Agency’s RDM Policy prompted changes to their Policy Statement on Ethical Conduct for Research Involving Humans, enabling “broad consent” for data sharing. Across the border, the National Institutes of Health and National Science Foundation are enacting new protocols for data management and sharing. While sensitive data management is an active discussion in academic circles, researched communities are not yet included. Community organizations collect and manage data and often act as intermediaries in human-participant research that produces sensitive data but do so in different ways than university researchers. These groups and their constituents stand to benefit from guidelines to help alleviate over-research, surveillance, and damage-centered narratives. Our IASSIST presentation will discuss insights and strategies gained from the RDM Community Data Toolkits Workshop on March 21-22, 2024. This two-day event will bring together researchers and information specialists alongside social justice organizations and non-profits to develop toolkits to better navigate the critical ethics of community research data. Discussions will ensure: • Data can be utilized by communities themselves in ways that serve their specific targets and metrics for change as conceived by their own determinants of need and designs for betterment. Community-led data practices empower and support community-led grassroots actions and initiatives. • Research data from communities is protected and does not make them vulnerable and/or grant them visibility, safeguarding their susceptibility to data misuse and exploitation. With the community-engaged interventions we hope to establish a framework to continue working on these toolkits beyond the workshop. By sharing these insights with the IASSIST community, we hope to assist researchers and information specialists who may also be working with community data and/or organizations, as well as find potential collaborators on future projects following this theme
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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 | 0.530 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.028 | 0.148 |
| Scholarly communication | 0.053 | 0.054 |
| Open science | 0.011 | 0.047 |
| Research integrity | 0.033 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".