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Record W2021046855 · doi:10.1016/j.polsoc.2009.05.002

Integrated land management in Alberta: From economic to environmental integration

2009· article· en· W2021046855 on OpenAlexaffabout
Keith Brownsey, Jeremy Rayner

Bibliographic record

VenuePolicy and Society · 2009
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of ReginaRoyal College of Physicians and Surgeons of CanadaMount Royal University
Fundersnot available
KeywordsChampionBoomObstacleRecessionPoliticsCompetition (biology)Land useEconomicsResource (disambiguation)Land-use planningNatural resource economicsEconomic growthPolitical economyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Alberta illustrates the obstacles to attempted policy transformation after years of deliberate policy drift. Despite being a pioneer of land use planning in western Canada, the province eventually relaxed its planning regime and failed to update plans that were perceived as an obstacle to resource-led development during a recession. When recession was succeeded by an oil-and-gas-driven boom, planning controls continued to be locally negotiated and relatively relaxed. The effect was to encourage damaging competition between resource industry and establish a pattern of clientilist politics, in which each industry looked to its departmental champion to resolve its land use problems. Whether the new provincial land use framework can change these deeply entrenched patterns or will merely layer new policies onto the old remains to be seen.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.006
Scholarly communication0.0080.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Citations12
Published2009
Admission routes2
Has abstractyes

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