MétaCan
Menu
Back to cohort
Record W4403093473 · doi:10.29173/cjnser688

Civic and Community Engagement among Poor Rural Women in Bihar: A Pilot Study

2024· article· en· W4403093473 on OpenAlexvenueno aff
Kirk A. Leach, Julien C. Mirivel, Tusty ten Bensel, Avinash Thombre

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCivic engagementCommunity engagementPublic engagementIntervention (counseling)Community developmentSocial engagementRural communityRural areaCommunity organizationSocioeconomicsPsychologyPolitical scienceEconomic growthSociologyPublic relationsSocial science

Abstract

fetched live from OpenAlex

Civic and community engagement is often crucial for the successful development of rural areas and a catalyst of personal transformation. This article examines changes in civic and community engagement among women in rural Bihar, India. Using an exploratory factor analysis of survey data from n = 815 respondents who participated in Heifer’s Values-Based Holistic Community Development [VBHCD] training, the study identifies three factors that constitute civic and community engagement. Next, the study assesses the efficacy of VBHCD’s impact on participants’ civic and community engagement relative to participants’ duration in the program and type of self-help group. The results indicate that pass-on groups are more civically active in their community than original groups. However, civic and community engagement wanes over the course of participation in Heifer’s intervention.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.351
GPT teacher head0.487
Teacher spread0.136 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCanadian journal of nonprofit and social economy researchSame topicCommunity Health and DevelopmentFrench-language works237,207