Participatory action research: The woven collective analysis approach to recognize experiential knowledge of poverty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.027 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it