Co-creating knowledge through co-operative inquiry:Using participatory writing to promote inclusion and solidarity within research, education and practice
Bibliographic record
Abstract
Our talk aligns with sub-theme 4 by showcasing collaborative writing that empowers diverse international voices, co-produces knowledge, enhances accessibility, and fosters social impact through inclusive, experiential, and social justice-oriented writing practices. This presentation synthesises 12 years of work developing a collaborative participatory writing framework that is in use by the International Network of Co-operative Inquirers (INCinq). In this presentation, members of the International Network of Cooperative Inquirers (INCInq) will introduce our evolving model of collaborative, participatory writing. The Network utilises online technologies to enable inquirers access to knowledge-generation and participatory writing methods. This process spans distance, time zones, professional experience and personal identities. The INCInq writing methodology fosters the inclusion of diverse experiential, practical, presentational and propositional knowledges and perspectives. INCInq has over 50 members, including educators and researchers from 8 countries, and leverages online technologies to enable international research partnerships. The writing framework that we will present facilitates the sharing of power and co-authoring of research. Individual members have also been using this collective writing technique as a pedagogical tool to encourage everyone in the learning and teaching space to share power and their voice. Furthermore, some social work practitioners have been using this approach to communicate and collaboratively document their practice wisdom with others. The presenters will outline our experience of how participatory writing advances co-creation, co-design and co-production to generate socially impactful outputs that promote social justice and wellbeing.
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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 | 0.092 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".