Non-Traditional Research Methods and Regional Planning Needs in Developing Countries: Is There an Ideal Methodology for Handling Critical Local and Regional Development Issues?
Bibliographic record
Abstract
First, a list of data and non-data limitations that complicate the methodological handling of critical regional problems in developing countries is highlighted. Then, this discussion is used to argue that the ideal methodology to deal with those complex development issues under such limiting conditions must have the following characteristics: a) be flexible so it can be applied under different sets of constraining factors; b) be issue specific so it can be used to target specific critical problems or combination of them; c) be cost-effective so that it can be used or replicated easily according to local conditions and needs; d) be simple so it can be adapted and disseminated by local actors; e) be holistic so it can deal consistently with the local and regional conditions at the same time; and f) be based on conjunctural causation so that the complex nature of the problem is more realistically reflected, and g) be theoretically sound so as to provide a scientific rational consistent or comparable with that of traditional research methodologies. Next, the advantages and disadvantages of traditional and non-traditional research methodologies are highlighted and compared to the requirements of the ideal methodology listed above in order to indicate that each of them fails one or more of those ideal characteristics; therefore, they are inconsistent with the nature of such ideal methodology. Soon after that, the type and nature of the research methodology that is consistent with the characteristics of the ideal research methodology for regional critical problems in developing countries is introduced and described. This ideal methodology, a new non-traditional methodology, to handle critical development issues is based on combining Rapid Assessment Research (RAR) and Qualitative Comparative Research (QCR) to identify and/or validate perceptions theories and practices related to the critical issue at hand using conjunctural thinking. Finally, it is concluded that with a simple non-traditional research tool based on the characteristics of the ideal methodology shared in this paper local and regional planners and decision-makers in developing countries could be able to support or reject scientifically existing official positions on local and regional social, economic and environmental development issues and discourse.
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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 | 0.100 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".