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Record W2732257134

Community-based Approaches to Resource and Environmental Management

2005· article· en· W2732257134 on OpenAlexaffabout
Susan Wismer, Bruce Mitchell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGovernment (linguistics)Context (archaeology)Public relationsPrivate sectorPolitical scienceBusinessCitizen journalismEnforcementEnvironmental planningEnvironmental resource managementEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

(from the Introduction) Community-based approaches are advocated widely internationally and domestically, based on the idea that information, understanding and capacity for action and change, as well as for monitoring and enforcement, do not reside only within government agencies or the private sector. Certainly, these competencies and others often do exist within communities, outside of identified scientific, planning and management organizations. Recent commentators have noted, however, that much of what has been written takes it as self-evident that locally-based participatory approaches result in fairer and more equitable decisions, more potential for generation of context-appropriate innovations, more effective implementation of plans and implementation strategies and a generally more sustainable world. Unfortunately, reports from the field make it clear that this is not always the case (Cooke et al., 2001)... As one contribution to discussion of the problems and potential of community-based approaches to resource and environmental management, this theme issue of Environments has gathered together accounts of the experience of researchers working at the local level on three continents – Asia, South America and Europe. We view this issue as an opportunity to share what is being learned from research conducted in countries or regions outside of Canada, providing an opportunity to reflect on transferability of experience and lessons learned.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.178
Teacher spread0.139 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations19
Published2005
Admission routes2
Has abstractyes

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