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Record W1499569449 · doi:10.3390/ijerph120707541

Environmental Resource Management in Borderlands: Evolution from Competing Interests to Common Aversions

2015· review· en· W1499569449 on OpenAlexafffundabout
Patrick H. Buckley, John Bélec, Jason Levy

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2015
Typereview
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsUniversity of the Fraser Valley
FundersGovernment of CanadaWestern Washington University
KeywordsDilemmaResource (disambiguation)EnthusiasmCommon-pool resourceValue (mathematics)Resource management (computing)Environmental resource managementPolitical scienceComputer scienceEconomicsPsychologySocial psychologyEpistemologyMicroeconomics

Abstract

fetched live from OpenAlex

Great enthusiasm is attached to the emergence of cross-border regions (CBRs) as a new institutional arrangement for dealing with local cross-border environmental resource management and other issues that remain too distant from national capitals and/or too expensive to be addressed in the traditional topocratic manner requiring instead local adhocratic methods. This study briefly discusses the perceived value of CBRs and necessary and sufficient conditions for the successful and sustainable development of such places. Then, assuming that necessary conditions can be met, the study investigates an intriguing hypothesis concerning the catalyzing of sustainable consensus for cross-border resource management based on a game theoretical approach that employs the use of dilemma of common aversion rather than the more traditional dilemma of competing common interests. Using this lens to investigate a series of events on the Pacific northwestern Canadian-American border in a part of the Fraser Lowland, we look for evidence of the emergence of an active and sustainable CBR to address local trans-border resource management issues. Although our micro-level scale fails to conclusively demonstrate such evidence, it does demonstrate the value of using this approach and suggests a number of avenues for further research.

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.142
GPT teacher head0.491
Teacher spread0.349 · 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 designOther design
Domainnot available
GenreReview

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
Published2015
Admission routes3
Has abstractyes

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