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Record W2045872518 · doi:10.5558/tfc83221-2

The urban public and forest land-use planning: Tapping into the majority

2007· article· en· W2045872518 on OpenAlexaffvenueabout
Scott Kidd, A. John Sinclair

Bibliographic record

VenueThe Forestry Chronicle · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPublic participationNormativePublic landUrban planningEnvironmental planningLand-use planningGeographyLand useForest managementEnvironmental resource managementBusinessPolitical scienceForestryPublic administrationEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Canadians desire involvement in forest management at normative or early planning phases. One way of accomplishing this is through meaningful public involvement in land-use planning efforts. The Provinces of Ontario and Manitoba have, respectively, completed or are completing the development of land use plans for large areas of forested landscapes. Both governments identified public participation as being an integral part of these processes. This paper examines how well these processes promoted participation by the general public, the vast majority of which resides in urban areas located outside the respective planning regions. It is determined that in both cases this was poorly done. Reasons are given for why and how increased participation by the urban public should be pursued. Key words: public involvement, land-use planning, forest management, urban centres, Lands for Life, East Side Planning Initiative, Canada

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.013
Scholarly communication0.0070.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.048
GPT teacher head0.287
Teacher spread0.239 · 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 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 routes3
Has abstractyes

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