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Record W2917054062 · doi:10.1093/heapro/daz004

Capacity building and personal empowerment: participatory food costing in Nova Scotia, Canada

2019· article· en· W2917054062 on OpenAlexafffundabout
Hiliary Monteith, Barbara J. Anderson, Patricia L. Williams

Bibliographic record

VenueHealth Promotion International · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMount Saint Vincent UniversityAcadia University
FundersAcadia University
KeywordsEmpowermentNova scotiaParticipatory action researchFood securityCapacity buildingCitizen journalismBusinessPolitical scienceEconomic growthGeographyEconomics

Abstract

fetched live from OpenAlex

Food insecurity impacts millions of people globally. It has been recognized as a priority and a human right by the United Nations where empowerment of women is identified as a significant goal in addressing food insecurity. In the Maritime Province of Nova Scotia (NS), Canada, more than one in five children live in food insecure households. Since 2002, participatory action research (PAR) has been an integral component of food costing in NS with an aim to support capacity building for food security. Building on earlier research that examined short-term outcomes, and recognizing a lack of research examining outcomes of PAR processes, this study aimed to explore the medium-term individual capacity building processes and outcomes of women involved in Participatory Food Costing (PFC). Findings revealed that capacities were built with respect to interrelated themes of 'awareness, participation, personal development, readiness to change, political impact, influence on others, self-esteem, project growth and project continuity'. In addition, the involvement of these women in PFC resulted in both personal empowerment and food security-related policy change. Involving vulnerable populations through PAR is valuable in influencing health-related policy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.257
GPT teacher head0.457
Teacher spread0.201 · 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

Citations9
Published2019
Admission routes3
Has abstractyes

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