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Two sides of the forest

2007· article· en· W2474551394 on OpenAlexaboutno aff
Ryan Bullock

Bibliographic record

VenueJournal of Soil and Water Conservation · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationGovernment (linguistics)General partnershipForest managementStumpageResource (disambiguation)Resource management (computing)ForestryBusinessEnvironmental planningCommunity forestryEnvironmental resource managementGeographyEnvironmental protectionPublic administrationPolitical scienceEnvironmental scienceFinance

Abstract

fetched live from OpenAlex

While conservation authorities are well-established in Ontario and have received international recognition (e.g. Krause et al. 2000), British Columbia community forests are less developed and have a shorter history. These two approaches have, however, shared similarities in policy and practice. They are similar in their orientation to forest, water, and soil resources; both tend to inherit degraded land bases; as local resource agencies, each holds an intermediate role between residents and senior governments; each has provincially assigned management rights over lands that represent significant, often contentious, community values. A main difference is that conservation authorities in Ontario represent a provincial-municipal partnership, in principle, based on provincial funding and technical support, while community forests are to be self-sufficient and pay Crown timber harvesting fees (or “stumpage”) to the province. The British Columbia government, with its community forest model, is pursing community forestry to provide economic opportunities for communities, not to create more parks. However, an expanded role for community forests in conservation, hazard management, and recreation is conceivable, given shifting public forest values and growing interest in local control (Robinson et al., 2001). Community forests Ongoing discussion of community forestry remains focused on substantive and …

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 score0.013
Threshold uncertainty score0.083

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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