Bibliographic record
Abstract
The book is the outcome of a research project ‘Management of Knowledge System in Natural Resources: Exploring Policy and Institutional Framework in Nepal’ undertaken by ForestAction Nepal with support from the International Development Research Centre (IDRC), Canada. When we completed the research project with a set of case studies and a review of theories related to knowledge systems and governance and shared the findings with a network of readers, we were excited to get very encouraging feedback. This encouraged us to compile the work as a book so that the empirical findings and insights emerging from the analysis could be disseminated to a wider audience. While preparing the case study reports, we realised that the insights could be potentially beneficial to the policy makers, researchers, planners and field practitioners for developing an understanding of the knowledge systems and their deliberative interface. This idea was materialised with a generous and continued support from IDRC. We hope that the compilation of case studies on natural resources, in the light of critical and theoretical insights, will help one understand the intricacies of knowledge systems as they relate to governance practices. There is indeed a continuing need for better understanding of the contexts, processes and outcomes of the production of knowledge and its application in various facets of governance of human society. In this context, our main goal of presenting the case studies in this book has been to understand how different systems of knowledge operate in the field of natural resource management, and what factors and conditions affect the process of deliberation among such knowledge systems.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.399 | 0.221 |
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".