Mixed cropping systems of Adivasi peoples in India using the domain analysis participatory technique
Bibliographic record
Abstract
Introduction Since the early 1990s, small grassroots development organizations have achieved notable successes with participatory research and development. As a result, national and even multilateral organizations have faced increasing pressure to also adopt participatory ideas and techniques. Now virtually all development organizations demand local participation on some level in at least some part of their project implementation. The benefits of participatory research and development systems such as Participatory Rural Appraisal (PRA) are well documented and supported (see for example Pratt 2001; Opp 1998; Whyte 1991). In brief, the research techniques are interactive, visual and tactile, so that anyone can participate regardless of age, social status or level of education. Secondly, participating people maintain ownership of their knowledge and of development processes. Furthermore, they are encouraged to use their knowledge to serve their own development needs rather than having an outside party decide what is good for them. The sense of ownership feeds into a third benefit, which is that “participation” is empowering for local people because they are looked upon as the experts harbouring valuable knowledge. In general, the results are that development projects are more appropriate in both scale and substance. Therefore, they are also more successful because they actually reflect the needs and wants of the stakeholders who are most impacted. However, as participatory systems are increasingly applied on a larger and larger scale, they are criticized for often falling short of meeting their ideal goals. The above mentioned benefits are only benefits if “participation” is enlisted from local people with the best intentions, behaviours and attitudes (Opp 1998). Critics are arguing that knowledge elicited using participatory methods are at best superficial due to rigid applications of techniques stripped of their theoretical underpinnings, a lack of investment in time, money and rigorous preliminary social research, and the alienation of knowledge from participants as it is taken to outsider “experts” for analysis. As a result, participants do not actually receive any benefit to participating, leading them to offer little support and even resisting research and development proposals. In other words, research and development projects are not achieving their potential for success (Chevalier and Buckles 2005; 2005b; Kapoor 2002; Li 2002; Campbell 2001; Pratt 2001; Gomez 1999; Sillitoe 1998; Opp 1998; Mosse 1998; 1994). Given the growing body of literature criticizing PRA and other existing systems of participatory research, Drs. Jacques Chevalier and Daniel Buckles developed the Social Analysis System (SAS) with funding from International Development Research Centre (IDRC) in Ottawa, Canada. SAS builds on the established legacies of its
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".