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Record W6929582510 · doi:10.48336/cxk4-xy62

Three keywords in the campaign against farmland consolidation and the loss of small farms through the lens of the Prince Edward Island Chapter of the National Farmers Union

2025· article· en· W6929582510 on OpenAlexaffabout

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConsolidation (business)EthnographyAttendanceAgricultureLand consolidation

Abstract

fetched live from OpenAlex

This thesis uses an ethnographic approach to examine campaigns against farmland consolidation and the loss of small farms through the lens of Prince Edward Island chapter of the National Farmers Union (NFU PEI). Using Raymond Williams’ Keywords concept, I trace three terms — “absentee landlords,” “family farm,” and “soil health”—as used by NFU PEI participants and their interlocutors at a time when loopholes to the Prince Edward Island (PEI) Lands Protection Act were publicly debated. Through this examination of meaning, I show how specific language used by participants and other groups illuminates underlying issues, debates, areas of consensus, and shifts in the agricultural landscape of PEI. These issues included contested claims to authenticity, remnants of colonialism, and the erasure of Temporary Foreign Workers, refugees and immigrants. Additionally, I show how an emphasis on issues such as heritage, obscure challenging questions around who owns land, who works the land and what makes “good” land. These arguments are supported by evidence from fieldwork in PEI from August 2021 to early June 2022, with additional meeting attendance in 2023 and 2024. Fieldwork consisted of library research, 15 interviews with NFU PEI members and members of related organizations, 8 farm visits with NFU PEI members and attendance at organization meetings, NFU conventions and other community meetings related to PEI land issues.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.025
GPT teacher head0.234
Teacher spread0.209 · 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
Published2025
Admission routes2
Has abstractyes

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