What stories to tell? A trilogy of methods used for knowledge exchange in a community-based participatory research project
Bibliographic record
Abstract
Researchers in the field of Aboriginal health generally have a keen interest in ‘participating in change’ to address the ongoing injustices experienced by Aboriginal peoples. Perhaps the most promoted methods for this purpose are those described as Indigenous methods and action research. Criteria of authenticity are generally used to assess the quality of research. In this essay, we reflect on how certain basic principles of action research, more notably ontological authenticity and educative authenticity can penetrate the process of knowledge exchange, creating spaces of ontological contamination and transformation. We reflect on the context of sharing ‘difficult knowledge’, knowledge that is encountered and shared in a post-colonial context of unequal power dynamics. We describe a trilogy of methods used for such knowledge exchange activities with three distinct audiences, and distinct goals. A commonality amongst the three described methods is the ‘unfinished’ and unorganised nature of what is transmitted, requiring the receptor to actively participate in the differentiation and reorganisation of information in a way that makes sense to him/her.
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 | 0.040 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.021 | 0.042 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| 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".