Bibliographic record
Abstract
This book addresses a key issue in higher learning, university education and scientific research: the widespread difficulty researchers, experts and students from all disciplines face when trying to contribute to change in complex social settings characterized by uncertainty and the unknown. More than ever, researchers need flexible means and grounded theory to combine people-based and evidence-based inquiry into challenging situations that keep evolving and do not lend themselves to straightforward technical explanations and solutions. In this book, the authors propose innovative strategies for engaged inquiry building on insights from many disciplines and lessons from the history of Participatory Action Research (PAR), including French psychosociology. The ongoing evolution of PAR has had a lasting legacy in fields ranging from community development to education, public engagement, natural resource management and problem solving in the workplace. All formulations have in common the idea that research must be done ‘with’ people and not ‘on’ or ‘for’ people. Inquiry of this kind makes sense of the world through efforts to transform it, as opposed to simply observing and studying human behaviour and people’s views about reality, in the hope that meaningful change will happen somewhere down the road. The book contributes many new tools and conceptual foundations to this longstanding tradition, grounded in real-life examples of collective fact-finding, analysis and decision-making from around the world. It provides a modular textbook on participatory action research and related methods, theory and practice, suitable for a wide range of undergraduate and postgraduate courses, as well as working professionals.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.058 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".