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Record W4253637211 · doi:10.24124/2015/bpgub1074

Perceptions of access to industrial forest land by Port Alberni recreational stakeholders.

2015· dissertation· en· W4253637211 on OpenAlexfundno aff
Andrew Ellis Dunbrack

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
FundersUniversity of Northern British Columbia
KeywordsRecreationRestructuringBusinessNegotiationForestryLand tenurePort (circuit theory)Land useDeforestation (computer science)Land managementEnvironmental resource managementEnvironmental planningGeographyFinancePolitical scienceEconomicsEngineeringAgriculture

Abstract

fetched live from OpenAlex

This thesis examines recreationalist experiences with industrial forest land access in the Port Albemi area of British Columbia where forest industry and land tenure restructuring has changed land management practices.Consequently, recreationalists' expectations of industrial forest land access are not met by current reality.A qualitative, exploratory case study examines historical and current recreational access barriers, their impact on recreationalists, and elucidates the success o f recreationalist-forestry company relationships in negotiating these barriers.Results indicate that recreational access restrictions, in the form of physical barriers, increased due to land tenure and forest industry restructuring.These restrictions have significantly limited recreationalists' activities and land use practices.As conceptualized by recreationalists accessing private land, now comprising the majority o f area forest land, restrictions suggest corporate land owner failure to meet social responsibilities.While successful recreationalist-forestry company relationships may offset the impacts of restrictions, these relationships have not fully replaced previous access regimes.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.349
GPT teacher head0.296
Teacher spread0.054 · 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
Published2015
Admission routes1
Has abstractyes

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