Mexican and Canadian case studies of community-based spatial information management for biodiversity conservation
Bibliographic record
Abstract
Sustainable development has come to summarize the acknowledged importance of non-destructive land-use. The idea has become widely accepted – perhaps because of its inherent constructive ambiguity, or perhaps because, like motherhood and apple pie, it is simply a notion that is hard to argue against. But unlike motherhood, it is not something to which an irrevocable commitment can arise from a moment of irrational passion and, unlike apple pie, it has no simple recipe. The challenge, as Geyer (1994) observes, is: How can dynamic communities with changing needs, aspirations and technologies maintain a non-destructive relationship with an environment that is itself dynamic and constantly changing? This clearly requires an adaptive process, and in the time frame that matters to us now, that adaptive process needs to be based on human intelligence and environmental information. Finding ways to optimize the use of available information and ensure that all providers and users of information have effective links to decision-making processes is an essential step towards sustainable development. GIS provides tools to discover, analyse and communicate the spatial relevance of data and information. A critical question still remains, however: How can high technology information management tools be brought into the public forum in a way that fosters fairness and increases decision-making competence (Webler 1995) rather than increasing polarization and marginalization?
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".