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Record W4229061994 · doi:10.4000/norois.11753

Une initiative agricole locale en appui à la sécurisation alimentaire ; le cas de “Cultiver pour nourrir” dans la Municipalité régionale de comté d’Antoine-Labelle (Québec)

2022· article· fr· W4229061994 on OpenAlexaffabout
Jessica Élie-Leonard, Mélanie Doyon

Bibliographic record

VenueNorois · 2022
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

L’objectif de cet article est de comprendre en quoi la présence d’initiatives agricoles locales contribue à la sécurité alimentaire en région rurale éloignée des grands centres. L’hypothèse principale suppose que les initiatives agricoles locales améliorent la sécurité alimentaire des populations les plus vulnérables des régions éloignées des grands centres compte tenu des difficultés de site et de situation. Dans un premier temps, l’article cherche à comprendre quelles dimensions de la sécurité alimentaire sont fragilisées sur le territoire de la municipalité régionale de comté (MRC) d’Antoine-Labelle. Ensuite, il s’intéresse à la contribution d’une initiative agricole, “Cultiver pour nourrir”, dont l’objectif est de promouvoir la sécurité alimentaire au sein de la municipalité de Mont-Laurier. La recherche montre notamment que cette initiative améliore la situation des organismes communautaires situés à proximité des jardins de “Cultiver pour nourrir”, en augmentant la disponibilité d’aliments frais et sains, en accroissant l’accessibilité économique et physique à ceux-ci et en bonifiant leur qualité. Les apports à la sécurité alimentaire demeurent toutefois relativement modestes et très saisonniers. Cependant, les retombées de l’initiative dépassent la question alimentaire et concernent également la cohésion et la mixité sociale.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.327
Threshold uncertainty score0.658

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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 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

Citations2
Published2022
Admission routes2
Has abstractyes

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