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Record W7006391993

Towards New Institutional Arrangements for Managing Forest Commons in Northwestern Ontario

2011· article· en· W7006391993 on OpenAlexaffabout

Bibliographic record

VenueDigital Library Of The Commons Repository (Indiana University) · 2011
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsLakehead University
Fundersnot available
KeywordsCommonsForest industryForest managementCommunity forestryLoggingIndigenousSustainabilityForesterGovernment (linguistics)Commodity
DOInot available

Abstract

fetched live from OpenAlex

"The forest industry has been the backbone of local economies in many remote locations in Canada. While this industry, which has focused on commodity products such as pulp, paper and lumber, thrived until the early part of this century, in recent years it has faced a major downturn that has resulted in extensive mill closures and unprecedented job losses to forest industry workers. Although municipalities that once benefited from the forest industry through employment and taxation are now experiencing negative social and economic impacts, Indigenous (First Nation) communities have generally been marginalized and historically received little benefit from the forest industry. This study examines the emergence of new institutional arrangements for the management of forest commons in northwestern Ontario (NWO) as an approach to improve the resilience of the communities that inhabit this vast boreal forest region. The study utilizes a qualitative approach based on semi-structured interviews with participants from 10 municipalities and 18 First Nation communities throughout NWO. The study participants include community leaders (mayors, chiefs, council) and key informants familiar with the forestry situation (former loggers and mill workers, lands and resources staff, and economic development officers). The study results have been used to formulate policy recommendations to develop a long-term economic vision to support sustainable local communities and the forest ecosystems that they depend on."

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 score0.372
Threshold uncertainty score0.521

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 communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.152
Teacher spread0.136 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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