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Record W1929265745 · doi:10.5558/tfc2012-136

Are community forestry principles at work in Ontario’s County, Municipal, and Conservation Authority forests?

2012· article· en· W1929265745 on OpenAlexaffvenueabout
Sara Teitelbaum, Ryan Bullock

Bibliographic record

VenueThe Forestry Chronicle · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsCentre for International Governance InnovationUniversité du Québec à Montréal
Fundersnot available
KeywordsCommunity forestryWork (physics)ForestryForest managementContext (archaeology)Corporate governanceCitizen journalismGeographyParticipatory managementEnvironmental planningEnvironmental resource managementSituatedLocal governmentBusinessPolitical sciencePublic administrationManagementEngineeringEconomics

Abstract

fetched live from OpenAlex

Ontario’s County, Municipal and Conservation Authority forests have received little attention within the academic literature on community forestry in Canada. These “Agreement Forests”, as they were once called, are a product of the early 20 th century and have been under local government management since the 1990s. Most are situated in Southern Ontario. In this article we investigate the extent to which community forestry principles are at work in these forests. Three principles— participatory governance, local benefits and multiple forest use—are analyzed using a composite score approach derived from survey data collected from nearly all of these forest organizations (response rate = 80%). Results indicate that most of these organizations do display attributes associated with community forestry principles, including a local governance process, public participation activities, local employment and multiple-use management. Traditional forestry employment is less strong than in similar studies of Crown land community forests; however, there is an important emphasis on non-timber activities. The article concludes that the County, Municipal and Conservation Authority forests represents a unique approach, which reflects the specific geographic and socio-economic context in which it resides.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.270
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.

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

Citations16
Published2012
Admission routes3
Has abstractyes

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