Embracing Ecological Learning and Social Learning: UNESCO Biosphere Reserves as Exemplars of Changing Conservation Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it