What People Call People: Language and Reflexivity in Participatory Research
Bibliographic record
Abstract
Background: Participatory research, which includes community-based research, patientoriented research, and citizen science, is an investigative approach that engages community stakeholders as partners in the research process. The increased prominence of participatory research in the world of science signals change, however, there is a noted absence of data that captures who academic researchers are engaging in participatory research, and what language is ascribed to community partners. Methods: Through Shift: Working for Change in Participatory Research, a survey invitation was sent to 5,480 principal investigators funded by Canadian Tri-Council agencies between 2013-2018. The survey included questions about community stakeholders and their research roles, remuneration, and researcher demographics. Open-text responses were inductively double-coded using in-vivo content analysis. Results: 1,005 survey respondents who conducted participatory research were asked two questions about community stakeholders: “Which community stakeholders were involved in this participatory research study?” and “What terminology do you generally use to refer to the community stakeholders on your team?”. In addition to selecting from a list of response options such as “community researchers”, and “peer researchers”, respondents provided 440 open-text responses. Terms referred to a variety of different community partners, including people in the education, arts, governance, health, industry, and social justice sectors. Conclusion: Academic researchers used multiple terms to refer to community collaborators. Knowing this, we hope to examine how power differentials are created and maintained through language, amplify voices that have been historically excluded from science, and advocate for researchers to be mindful of language choices when referring to community partners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.353 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.023 | 0.209 |
| Scholarly communication | 0.036 | 0.042 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".