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

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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 score0.099
Threshold uncertainty score0.378

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 communication0.0000.000
Open science0.0000.000
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 head0.063
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