Resilience and Social-Ecological Systems: \nThe UNESCO Biosphere Reserve Program in Australia and \nCanada
Bibliographic record
Abstract
United Nations Educational, Scientific and Cultural Organisation (UNESCO) Biosphere Reserves \n(BRs) provide an example of an integrated sustainability framework that allows for connection \nbetween international, national, state / provincial and local levels of conservation and capacitybuilding. \nThe three major functions of a BR are conservation of biodiversity, sustainable development \nand support for logistics. As coupled social-ecological systems, BRs explicitly acknowledge that \nhuman systems and ecological systems are inextricably linked, and have the potential to bridge \necological and social-political spheres that have been viewed as predominantly disparate entities, \nrather than as interconnected or nested systems. \nThe aim of this thesis is to identify the key features (assets, process and outcome) required to \nenhance the fit between governance systems and ecosystems using the UNESCO BR model, and \ndevelop a framework for establishing BRs as resilient working landscapes. By identifying features that \nseem critical for linking civil society, institutions and government dynamically across multiple levels, \nthe research addresses the governance dimension of ecosystem management and the social factors \nthat enable such management. The scope of the thesis is limited to developed country contexts. \nData are derived from focus groups, site visits, 52 key informant interviews and literature reviews. \nThe research process utilised an emergent, naturalistic inquiry, characterised by abductive, deductive \nand inductive methods. Four Australian and four Canadian qualitative case studies support and \ndemonstrate the three phases of the BR resilience conceptual framework developed herein. \nUNESCO BRs originated in the early 1970s as international examples of biodiversity conservation \nand sites of scientific research and monitoring. Since this time, the international program has \nbroadened to include more complex notions of social-ecological systems, reflecting shifts in \nenvironmental discourse and praxis. The Australian BR Program is characterised by governmentinitiated \nBRs and those generated though community-derived stewardship. Over the same period, the \nCanadian BR Program has consistently developed through community capacity and the Canadian \nBiosphere Reserve Association. \nCapital assets and ‘new governance’ processes are two of the three key phases of developing a \nsuccessful (resilient) BR. Adaptive capacity is a key component of the final phase; the achievement of \na resilient working landscape. In the framework, evolution and devolution of a BR occurs in response \nto social and ecological variables. However, maintenance and renewal of capital assets are crucial to \nsustaining the first and most fundamental phase of BR resilience. \nSpecific guidelines for the application of the BR resilience conceptual framework are provided to \ninform individual BRs and their national programs more generally, and provide any party interested in \nthe BR concept with a means to develop a resilient BR, from its inception. Avenues for future \nresearch are suggested, with a recommended focus upon harnessing greater understanding of \nresilience factors in social-ecological systems, and the relationship of these to BRs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".