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Record W4403660211 · doi:10.1016/j.wss.2024.100227

The contributions of community seed saving to health and wellbeing: A qualitative study in Thunder Bay, Canada

2024· article· en· W4403660211 on OpenAlexaffabout
Rachel L. W. Portinga, Charles Z. Levkoe, Lindsay P. Galway

Bibliographic record

VenueWellbeing Space and Society · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsLakehead University
Fundersnot available
KeywordsThunderBayQualitative researchCommunity healthGeographySociologyPsychologyMedicinePublic healthSocial scienceArchaeologyNursingMeteorology

Abstract

fetched live from OpenAlex

• Community seed saving positively contributes to health and wellbeing • Community seed saving expanded health benefits beyond those in community gardening • Benefits included more physical movement and myriad positive emotions • Benefits included improved engagement with ecosystems, communities, and individuals • Community seed saving should be considered as a health promotion intervention This paper positions community seed saving (CSS) as collective knowledge and practices used to cultivate, collect, conserve, exchange, and advocate for regionally adapted seeds as a foundation of healthy and sustainable food systems. Qualitative research involved twelve interviews with community seed savers in Thunder Bay, Canada. We explored the relationships to participants’ health and wellbeing through themes of physical health, mental, emotional, and spiritual health, and relationships and reciprocity. The findings illustrate that CSS can directly benefit individuals and influence social and ecological determinants of health. We argue that public health should consider CSS a health promotion intervention and an important future direction for research.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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 designQualitative
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
Published2024
Admission routes2
Has abstractyes

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