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Record W3096770835 · doi:10.3390/land9110424

Knowledge Mobilization in the Beaver Hills Biosphere, Alberta, Canada

2020· article· en· W3096770835 on OpenAlexafffundabout
Clara-Jane Blye, Elizabeth Halpenny, Glen T. Hvenegaard, Dee Patriquin

Bibliographic record

VenueLand · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology, Conservation, and Geographical Studies
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBeaverBiosphereAutonomyAgency (philosophy)ScarcityEnvironmental resource managementMobilizationNature reserveGeographyEnvironmental planningBusinessPolitical scienceEcologySociologyArchaeologySocial scienceEconomicsBiology

Abstract

fetched live from OpenAlex

This study explores how knowledge was and is mobilized to advance the objectives of the Beaver Hills Biosphere Reserve, located in Alberta, Canada. Established in 2016, a 12-year collaborative effort worked to establish the biosphere reserve and achieve formal UNESCO designation. Subsequent efforts to grow the newly established biosphere reserve have accelerated in recent years. Our study documented how different types of knowledge were accessed, created, curated, and shared between partners during these two time periods. Focus group interviews were conducted with 14 participants, who are affiliated with Beaver Hills Biosphere Reserve partner organizations, and revealed the following findings: (1) not all knowledge is equally valued or understood; (2) partnerships are highly valued, and were essential to successful knowledge mobilization, but were stronger among individuals rather than organizations; (3) fear of the loss of autonomy and potential complications due to the establishment of a biosphere reserve slowed the exchange of information and engagement by some regional actors; and (4) knowledge mobilization is and was impeded by staff and agency capacity, finances, and time scarcity. This was further complicated by entrenched norms of practice, existing successful working relationships impeding the development of new partnerships, and embracing alternative forms of knowledge.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.453

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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 designObservational
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

Citations3
Published2020
Admission routes3
Has abstractyes

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