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Record W4240748963 · doi:10.24124/2016/bpgub1145

Farmland protection in British Columbia: an evaluation of the Agricultural Land Commission's application process and its impact on long-range agricultural land use planning

2016· dissertation· en· W4240748963 on OpenAlexaffabout
Lou-Anne Daoust-Filiatrault

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsMcGill UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsCommissionAgricultureLegislatureAgricultural landLand-use planningProcess (computing)Statement (logic)Environmental planningLand useRange (aeronautics)Political scienceBusinessGeographyEngineeringCivil engineeringLawComputer scienceArchaeology

Abstract

fetched live from OpenAlex

There is concern that agricultural land use planning in British Columbia (BC) has become too focused upon and driven by the Agricultural Land Commission's (ALC) application process. According to Richard Bullock, former Chair of the ALC, too much prominence has been given to the application process and not enough to long-range planning. Given the importance of Bullock's statement and the potentially significant implications, it is critical to further investigate Bullock's assertions. Therefore, the purpose of this research is to analyze Bullock's statement and assess whether the ALC is driven by the application process and the extent to which it supports or undermines long-range agricultural land use planning. Data were collected through a content analysis of ALC application archives, Agricultural Advisory Council (AAC) meeting minutes for the City of Kelowna, and key informant interviews with planning professionals. Results reveal that Bullock's statement is valid, but only to a limited extent because the application process does not hinder long-range planning for farmland protection in BC the current legislative framework hinders it. The results of this study expose how the effects of a few legislative details have had the double effect of increasing the prominence of applications and constraining what long-range planning that the ALC can do. --Leaf 2.

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)0.010
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0090.004
Scholarly communication0.0060.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.270
Teacher spread0.254 · 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 designQualitative
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
Published2016
Admission routes2
Has abstractyes

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