Participatory action research: The woven collective analysis approach to recognize experiential knowledge of poverty
Bibliographic record
Abstract
When conducting Participatory Action Research (PAR), we risk invalidating the experiential knowledge of people in poverty. Their contributions might only be seen as legitimate when put through a formal PAR process. We have thus developed a “woven collective analysis” approach, intertwining experiential, practical and academic knowledge. Diverse stakeholders reflect together and combine their voices, while ensuring that the experiential knowledge of people living in poverty remains the primary focus. Using the weaving process as a metaphor and a food-autonomy project as an example, we explore the steps involved in this data analysis approach: warping (or the need to recognize different types of knowledge and identify the actions required to use and communicate them); threading (or how to put into place a series of frameworks to allow information on social patterns to emerge, while combining varied knowledge); and sleying (or using targeted collective analysis to tighten up the information, in a recurring and systematic way). These combined operations contribute to the weaving process and the emergence of a new fabric of complex, social and transformational Common knowledge.
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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 | 0.114 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| 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".