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Record W7079493805 · doi:10.26108/ks8t-jg26

Impact on medium-term individual capacity building from involvement in Participatory Food Costing

2011· article· en· W7079493805 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2011
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsCapacity buildingFood securityNova scotiaParticipatory action researchCitizen journalismQualitative researchResource (disambiguation)

Abstract

fetched live from OpenAlex

Food insecurity is a multifaceted issue that requires further attention in Nova Scotia and throughout the world. Capacity building is considered to be a strategy for improving food security and is suggested to be a benefit of participatory processes. The purpose of the present study was to explore the individual capacity building processes and outcomes of medium-term involvement, defined as greater than four years, in Participatory Food Costing. Seven participants of the Nova Scotia Participatory Food Costing Project who had been involved for at least four years were recruited for this study. All participants were Nova Scotian women and most were affiliated with their local Family Resource Centres. Semi-structured telephone interviews were conducted with each participant and the questions followed the adult learning model. The data were collected, transcribed and coded using the phenomenology approach of qualitative research. Nine main themes emerged through analysis of the interviews: awareness, personal development, participation, readiness to change, influence on others, political impact, self-esteem, project continuity and project growth. These themes were consistent with the dimensions of capacity building that have been outlined in the published literature, indicating evidence for capacity building. The findings of this study demonstrate that capacity building processes and outcomes of those involved in Participatory Food Costing can contribute to solving food security related 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)0.001
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 score0.050
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.136
GPT teacher head0.284
Teacher spread0.148 · 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
Published2011
Admission routes1
Has abstractyes

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