Building communities of learning: Indigenous ways of knowing in contemporary natural resources and environmental management
Bibliographic record
Abstract
In this paper, we explore the emergence of what we term ‘communities of learning’ within the context of natural resources and environmental management (NREM). These communi-ties reflect new forms of interaction and cooperation between NREM decision makers that bring together the unique contributions of indigenous ways of knowing alongside academic and scientific approaches. Here, ‘indigenous ways of knowing’ refer to how indigenous and local peoples cultivate knowledge in the context of NREM (Berkes 2009 this issue). This is a ‘place-based’ process that embodies knowledge of species, livelihood practices and cultural beliefs, values and norms. In NREM, as in other fields, indigenous cultures have often been viewed through a binary lens, either as an impediment to socioeconomic progress, or as static packages of knowledge, belief and practice that must therefore be preserved from homogenising pressures, such as globalisation (Marglin 1990; Arce & Long 2000; Sen 2004). Rarely have indigenous ways of knowing been recognised as adaptive, dynamic assets for building diverse development trajectories that reflect local needs and aspirations. Where this has occurred, however, the shift in thinking has enabled researchers to explore knowledge as an adaptive cultural ele-ment, as well as encourage a much more practical engagement between indigenous groups, researchers and policy makers/managers. In doing so, it allows for place-based alternatives for both research and management policies in contemporary cross-cultural settings (Sillitoe 2006; Davidson-Hunt & O’Flaherty 2007). In this instance, indigenous groups are our focus. However, communities of learning may organise around other resource-dependent groups for the construction of ‘place-based’ knowledge. In a management context, the ability of different actors to meaningfully contribute to creat-ing solutions shifts in relation to the authority they are able to claim against competing world views and fields of action. ‘Communities of learning’ result from the recognition that each community possesses unique knowledge and resources that can contribute to management decisions. Figure 1 illustrates the potential convergence of these dominant players to form communities of learning.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".