“Ignoring” to “Autonomous” Participation: Narratives of a Participatory Action Researcher
Bibliographic record
Abstract
This paper examines different layers of participation while conducting Participatory Action Research (PAR). In the journey of three years of fieldwork with teachers, many realizations were made about becoming co-researchers and engaging in a collaborative knowledge-building process for developing an engaged pedagogical approach. The paper had two purposes: a) exploring the different layers of participation in PAR, and b) documenting the lead researcher’s continuous professional learning in understanding PAR. The lead researcher proposed “ignoring” to “autonomous” participation as levels. The lead researcher also changed from overly influencing roles on PAR to accepting co-researchers' voices and respecting their efforts for sustainable change.
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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 | 0.080 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.071 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".