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Exploring the Federal Role in Protecting Canada’s Farmland: A Matter Worthy of National Interest?

2020· article· en· W3091217780 on OpenAlexaffvenueabout
David J. Connell

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsLegislationFederalismGovernment (linguistics)Public administrationCooperative federalismBusinessPolitical sciencePublic economicsEconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

Is protecting farmland a matter of national interest? If so, should the federal government play a stronger role in agricultural land use planning (AgLUP)? This paper examines potential roles and contributions of the federal government in AgLUP. Methods were based on surveys with key informants that examined the validity and viability of six possible roles of the federal government. The key informants were provincial-level experts in AgLUP from across Canada. We found that all six of the potential roles of the federal government to protect farmland that we identified are, to varying degrees, valid and reliable options. Two of the six roles were viewed most favourably: co-operative federalism; integrated policy approach. We also identified a seventh role, which is for the federal government to adopt a policy that ensures that decisions regarding the use of federal-owned land must adhere to provincial legislation.

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)0.013
metaresearch head score (Gemma)0.025
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 score0.086
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0130.008
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.002
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 head0.063
GPT teacher head0.233
Teacher spread0.170 · 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
Published2020
Admission routes3
Has abstractyes

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