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Record W7008749788

Changing relations of agricultural land tenure and access in the Canadian Prairies

2022· dissertation· en· W7008749788 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsLand tenureGovernment (linguistics)Land lawNegotiationIndigenousLand managementCustomary landProperty rightsCommunal landPrivate property
DOInot available

Abstract

fetched live from OpenAlex

Amid trends of privatization, financialization, and decreasing access to agricultural land, there is a call for more sustainable and equitable land tenure and access. In response, I present four cases as interventions into the story of private property in the Canadian prairies, asking how stakeholders negotiate the multiple and sometimes competing functions of agricultural land in economic development, food production, conserving and enhancing ecological resources, recreation, and reconciliation. In qualitative studies of a) persuasive stories used by respondents to government consultations on land ownership to foster change b) public responses to changes in trespassing legislation, c) conflict and collaboration among stakeholders managing land for agri-environmental goals in alternative grazing land tenure models and d) a network of settler landholders sharing land with Indigenous land users, I employ critical realism to analyse interviews and secondary data. I consider questions of rights and responsibilities to land, mechanisms of inclusion and exclusion from land, the public good, and the discourse and actions that challenge or legitimize land access/tenure practices and related policies/legislation. Each case also explores the possibility of different futures for land regimes based on changing social relations as people work to challenge or further entrench private property rights. Alternatives to private land ownership cultivate diverse relationships in relation to, and with, land and people.

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 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 score0.616
Threshold uncertainty score0.621

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 studies0.0010.000
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 head0.013
GPT teacher head0.193
Teacher spread0.180 · 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 teacher head, 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

Citations0
Published2022
Admission routes1
Has abstractyes

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