Participant-Making: bridging the gulf between community knowledge and academic research
Bibliographic record
Abstract
Too often in social research for design, academic knowledge is privileged at the expense of other knowledge and ways of knowing, although by overlooking insights from other participants this academic meaning-making may be wasteful and/or damaging to relations. In this paper, we describe a project that focuses on establishing academic/community relations to look at how knowledge issues are handled in setting up participative projects. We touch on the ethics of the ‘informed consent’ required for the ethics approval process and that of generating and sharing project outcomes in a way that reflects team membership, considering how to share credit, encourage diverse opinion and ensure some value in participating for all participants. Since a key outcome of the study is intended to be policy recommendations as to how to involve community groups in research projects, we take a highly reflexive approach. We reflect here on how we, as academic researchers, became participants and what we made available to our partners in research to do the same.
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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 | 0.315 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.072 |
| Scholarly communication | 0.028 | 0.046 |
| Open science | 0.007 | 0.046 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".