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Record W7037856317

Fostering Connections Amidst and Beyond the Pandemic: Community-Based Research and Food Sovereignty in Fredericton, New Brunswick

2021· dissertation· en· W7037856317 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2021
Typedissertation
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Management
Canadian institutionsnot available
Fundersnot available
KeywordsFood sovereigntySovereigntyProcess (computing)Space (punctuation)Focus groupEthics of careAppreciative inquiry
DOInot available

Abstract

fetched live from OpenAlex

Although there is a strong call for engaged and decolonized research in cultural anthropology, very few structures are in place in universities to train and encourage graduate students in carrying research projects that take up these challenges. Born from the successful research collaboration between a Master’s student and Hayes Farm, a community farm in New Brunswick, this thesis aims to prove that change in research practices is both needed and possible. The research team used a community-based research approach and digital methods, including interviews, surveys and appreciative inquiry, to investigate how the community farm model could foster food sovereignty in New Brunswick. In parallel, a prominent focus was brought to the collaborative research process itself and to the nature of relationships, which are placed at the center of the Hayes Farm’s mission. A feminist ethics of care and an action-research framework were used to make sense of the research’s process and results. Key findings include that a community farm can best advance food sovereignty through its role as a connector and a healing space for the community, and that the relationships that are created and enacted through collaborative research are as much important as the outcomes that it produces in terms of knowledge.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.007
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 head0.209
GPT teacher head0.427
Teacher spread0.219 · 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.

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
Published2021
Admission routes1
Has abstractyes

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