Bibliographic record
Abstract
Fully revised and updated, this second edition of Participatory Action Research (PAR) provides new theoretical insights and many robust tools that will guide researchers, professionals and students from all disciplines through the process of conducting action research ‘with’ people rather than ‘for’ them or ‘about’ them. PAR is collective reasoning and evidence-based learning focussed on social action. It has immediate relevance in fields ranging from community development to education, health, public engagement, environmental issues and problem solving in the workplace. This new edition has been extensively revised to create a user-friendly textbook on PAR theory and practice, including: updated references and a comprehensive overview of different approaches to PAR (pragmatic, psychosocial, critical); more emphasis on the art of process design, especially in complex social settings characterized by uncertainty and the unknown; developments in the use of Web2 collaborative tools and digital strategies to support real-time data gathering and processing; updated examples and stories from around the world, in a wide range of fields; critical commentaries on major issues in the social sciences, including stakeholder theory, systems thinking, causal analysis, monitoring and evaluation, research ethics, risk assessment and social innovation. This modular textbook provides novel perspectives and ideas in a longstanding tradition that strives to reconnect science and the inquiry process with life in society. It provides coherent and critical treatment of core issues in the ongoing evolution of PAR, making it suitable for a wide range of undergraduate and postgraduate courses. It is intended for use by researchers, students and working professionals seeking to improve or rethink their approach to co-creating knowledge and supporting action for the well-being of all.
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 | 0.066 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.023 |
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".