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Record W7056073373

Effectiveness of dispute resolution mechanisms in natural resource management in Ontario

2017· dissertation· en· W7056073373 on OpenAlexfundaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
FundersYork UniversityStrongLakehead University
KeywordsPremiseNatural resource managementNatural resourceResource management (computing)Process (computing)Resource (disambiguation)Variety (cybernetics)
DOInot available

Abstract

fetched live from OpenAlex

For a variety of reasons, the number and complexity of natural resource management
\nconflicts in Ontario is increasing rapidly. To evaluate the premise that existing natural
\nresource management planning processes are ineffective in preventing and/or resolving
\nnatural resource use and management disputes, fifteen cases of natural resource conflict
\nprevention/resolution processes in Ontario were studied. The public
\nconsultation/participation guidelines outlined in the Ministry of Natural Resources? Timber
\nManagement Planning Manual for Crown Lands in Ontario (OMNR 1986a) were used as one
\nof the cases and as a benchmark for the comparison of other cases. Based on the analyses,
\ncharacteristics, pros, cons and effectiveness of each prevention/resolution process studied
\nwere described. Conclusions support the premise and suggest that effective conflict
\nprevention/resolution processes must have the ability to be modified to accommodate unique
\nconflict characteristics such as type and source of conflict. Processes which allow for
\nvarying levels of public participation and consultation tend to be most adaptable to necessary 
\nmodifications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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