Embracing Ecological Learning and Social Learning: UNESCO Biosphere Reserves as Exemplars of Changing Conservation Practices
Bibliographic record
Abstract
Biosphere reserves were first created in 1976 to help scientists, managers, and communities better understand how to conserve biodiversity and improve human-environment interactions. Since then, biosphere reserves have evolved from a primary focus on 'ecological learning' to a broader orientation that includes 'social learning'. The purpose of this paper is to trace how this shift became intertwined with changing expectations about the purpose and philosophy, criteria for site selection, and assessment of effectiveness of biosphere reserves as exemplars of conservation and sustainable development. Drawing on academic reports, policy and other archived documents from the international and Canadian programs, and interviews of key participants, this paper examines how international priorities changed and became expressed on the ground in designation processes and research practices of Canadian biosphere reserves. Our research indicates that social dimensions of learning have been added to earlier ecological objectives. This addition has had a dual impact. While laudably broadening perspectives on research, learning, and learners to include social scientists and local people more effectively, a heightened emphasis on social dimensions has increased the complexity of anticipated outcomes tied to governance and social goals. Biosphere reserves must now establish research and management approaches that encompass both ecological and social dimensions of learning reflecting collaborative and interdisciplinary research and practice that include local perspectives and assessment goals. These changes may require improved clarity for determining where future biosphere reserves should be created and how they should be managed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".