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Record W7065719281

Exploring Governance in Canadian Ramsar Sites to Ensure their Sustainability

2020· other· en· W7065719281 on OpenAlexaboutno aff

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2020
Typeother
Languageen
FieldPhysics and Astronomy
TopicElectrical and Electromagnetic Research
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityRamsar siteCorporate governanceEnvironmental governanceWetlandConventionPublic participation
DOInot available

Abstract

fetched live from OpenAlex

The Ramsar Convention came into effect in 1975, in response to global losses of wetland habitats and their ecological services. Canada joined the Convention in 1981. As essential elements of sustainability, this research examined the types of governance and management activities used in the 37 Canadian Ramsar sites. How ecosystem governance could further support environmental sustainability was also explored. Ramsar sites were assessed using sustainability indicators, looking at the Ramsar Convention 14 priority areas of focus such as presence of co-management structures, management plans, and monitoring programs under the three commitment criteria (wise use, management, cooperation). The results showed a large variation in terms of management plans, governance structures and reporting procedures with some sites, such as Old Crow Flats, having high sustainability scores while others, such as Southern James Bay, with low scores. Reasons for variation related to the lack of updated management plans and inadequate monitoring and reporting programs. Sustainability science provides linkages between ecological and social systems, underpinned by participatory and collaborative governance structures. Canadian Ramsar sites provide a living example of how social-ecological characteristics should be integrated to ensure sustainability.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.015
GPT teacher head0.189
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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