Action Research: An Essential Approach for Constructing the Development of Sustainable Urban Agricultural Systems
Bibliographic record
Abstract
How can research contribute more directly to promoting and leading to sustainable solutions and projects? This article suggests that one of the most important research approaches capable of achieving this is the Action Research approach. This involves the researcher taking on a number of roles when working with other actors (e.g. citizens, farmers, local elected officials, citizen associations, government representatives. . . with the specific set of actors depending upon the nature of the subject being investigated and for which solutions are sought). The roles that the researcher can play involve providing appropriate information to the other actors, providing counseling to them, organizing and animating meetings with the actors, and accompanying the whole process involving all the actors. These roles are essentially played out by the researcher when the other actors request the researcher to assume whichever roles they consider to be significant. The fundamental notion is that through this process the actors appropriate the sustainable solutions as their own, and the researcher helps them achieve this. This article is based on: a) a synthesis of pertinent research using the Action Research approach (specifically in relation to sustainable agricultural systems in periurban territories), and b) specific research undertaken by the two co-authors of the article, all in the context of periurban agricultural systems during the last 8 years, as well as on some of their publications. The necessary characteristics of Action Research and the researchers involved are identified, namely: a) patience; b) an emphasis on process; and c) an emphasis on participation on the part of multiple actors.
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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 | 0.099 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.012 | 0.117 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".