Bibliographic record
Abstract
Research can facilitate mutual learning, allow participants’ voices to be heard, increase practical usefulness of studies and foster empowerment. This paper discusses ways that groups can take part in research, outlines advantages and limits of each and explores strategies for enhancing benefits. This content is illustrated with brief examples from recent research publications and from two longer case studies. Groups and members can be involved as participants or co-producers of research. As participants, they either act as research subjects by contributing data, or as collaborators who are consulted at various times to help keep a study relevant to community issues. Being a subject offers an opportunity to reflect and share views, while collaborators and researchers can learn from working together. Though collaborators can exert influence, they have little control over decisions around focus, design, methods or dissemination of a study. Co-producing knowledge offers community groups more power, learning and empowerment but requires high levels of mutual trust, commitment and persistence. Potential gains and risks increase as involvement intensifies. However, researchers can enhance benefits at any level, by keeping this goal in mind when planning studies.
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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 | 0.140 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".