Enhancing innovation between scientific and indigenous knowledge: pioneer NGOs in India
Bibliographic record
Abstract
BACKGROUND: Until recently, little attention has been paid to local innovation capacity as well as management practices and institutions developed by communities and other local actors based on their traditional knowledge. This paper doesn't focus on the results of scientific research into innovation systems, but rather on how local communities, in a network of supportive partnerships, draw knowledge for others, combine it with their own knowledge and then innovate in their local practices. Innovation, as discussed in this article, is the capacity of local stakeholders to play an active role in innovative knowledge creation in order to enhance local health practices and further environmental conservation. In this article, the innovative processes through which this capacity is created and reinforced will be defined as a process of "ethnomedicine capacity". METHODS: The field study undertaken by the first author took place in India, in the State of Tamil Nadu, over a period of four months in 2007. The data was collected through individual interviews and focus groups and was complemented by participant observations. RESULTS: The research highlights the innovation capacity related to ethnomedical knowledge. As seen, the integration of local and scientific knowledge is crucial to ensure the practices anchor themselves in daily practices. The networks created are clearly instrumental to enhancing the innovation capacity that allows the creation, dissemination and utilization of 'traditional' knowledge. However, these networks have evolved in very different forms and have become entities that can fit into global networks. The ways in which the social capital is enhanced at the village and network levels are thus important to understand how traditional knowledge can be used as an instrument for development and innovation. CONCLUSION: The case study analyzed highlights examples of innovation systems in a developmental context. They demonstrate that networks comprised of several actors from different levels can synergistically forge linkages between local knowledge and formal sciences and generate positive and negative impacts. The positive impact is the revitalization of perceived traditions while the negative impacts pertain to the transformation of these traditions into health commodities controlled by new elites, due to unequal power relations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".