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Record W4392199902 · doi:10.1007/s44282-024-00040-0

Scope and challenges in the implementation of Time Bank in India: a qualitative study

2024· article· en· W4392199902 on OpenAlexaff
Ankita Verma, Sruthi Sridhar, Kaneez Fatima Dar, Manish Kumar Asthana

Bibliographic record

VenueDiscover Global Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsMount Allison University
FundersIndian Institute of Technology Roorkee
KeywordsScope (computer science)Thematic analysisPublic relationsQualitative researchSustainabilitySocial capitalBusinessPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

Abstract This paper explores the scope of services and challenges in implementing Time Bank in India. This community-based initiative allows people to exchange skills and services without the involvement of money. A qualitative study was conducted using semi-structured telephone interviews with 20 participants aged 18 to 35. Thematic analysis revealed four major themes: Services, Challenges, and Limitations of Time Bank, Factors Affecting Help-Seeking Behavior, and Community and Gender Dynamics. Participants expressed interest in offering a wide range of services, from household tasks to professional services. However, challenges and limitations were also identified, such as a lack of awareness and understanding of the concept, social stigma, and concerns about trust, privacy, and safety issues. The study underscores the need to consider cultural and social factors while implementing community-based initiatives. Despite these challenges, participants believed Time Bank could be a valuable platform for building social connections and community support. The study’s implications highlight the need for further research on the impact of Time Bank on social capital and the sustainability of the initiative in different cultural contexts.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.998

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 integrity0.0000.000
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 head0.029
GPT teacher head0.393
Teacher spread0.364 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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