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Record W1123907543

Strategic Methods in Community Engagement for UNESCO Biosphere Reserves

2011· article· en· W1123907543 on OpenAlexaboutno aff
Kellee Jackson, P. Johnson, Melinda Jolley

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsnot available
Fundersnot available
KeywordsBiosphereTransparency (behavior)Openness to experienceContext (archaeology)Community engagementPolitical scienceEnvironmental resource managementSustainable developmentCorporate governanceEnvironmental planningStrategic environmental assessmentGeographyStrategic planningBusinessPublic relationsEcologyEnvironmental impact assessmentEconomicsMarketingPsychologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

This research aimed to find strategic methods in community engagement related to regional sustainable development, specifically within the context of regions in Europe and North America that are applying for the UNESCO Biosphere Reserve designation. The Framework for Strategic Sustainable Development was presented as a planning framework that can fill gaps in the current Biosphere Reserve planning process. A tool for assessing community engagement based on the five Process Characteristics of transparency, cooperation, openness, inclusiveness, and involvement was created and used to explore community engagement practices in six UNESCO Biosphere Reserve regions in Sweden and Canada. The assessment of methods used in those six regions yielded a list of nine methods which stood out in contributing to community engagement.

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)0.064
metaresearch head score (Gemma)0.087
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: none
Teacher disagreement score0.064
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.087
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.021
Science and technology studies0.0060.006
Scholarly communication0.0140.012
Open science0.0020.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.177
GPT teacher head0.357
Teacher spread0.180 · 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
Published2011
Admission routes1
Has abstractyes

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