Research Responsibility Agreement: a tool to support ethical research
Bibliographic record
Abstract
When engaging in community-based research, it is important to consider ethical research practices throughout the project. While current research practices require many investigators to obtain approval from an ethics review board before starting a project, more is required to ensure that ethical principles are applied once the investigations begin and after the investigations are complete. In response to this concern, as expressed by workers at a feminist non-profit during a community placement, we developed a tool to foster both greater ethical and feminist research practice in community-based research. Using feminist theories, methodologies, and concepts such as epistemic justice, epistemic trust, and coauthorship, a tool was developed to support researchers and other collaborators in building relationships of reciprocity. This tool, called the Research Responsibility Agreement (RRA) invites all members of a research project to explicitly reflect on their role in the research, their relationships with other collaborators, their responsibility to contributing meaningfully in the project, and their plans to remain accountable to one another. In doing so, the RRA adds to existing tools that support ethical research by sharing explicit reflections from all collaborators on how to prevent harm and by asking them to reflect on ethical practices beyond the initial stages of the project. The RRA also encourages greater engagement from researchers and collaborators toward building meaningful relationships with each other, and with participants, to work together in advancing social change. As a practical tool that promotes reflection, that builds relationships, and that holds all parties accountable to ethical and feminist research practices, the RRA has the potential to generate impactful change in community-based research projects and beyond. While the RRA is tailored to community-based research, it can be applied widely to any research project and has the potential to revolutionize how research relationships are built across disciplines.
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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 | 0.189 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.038 | 0.026 |
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".