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Work, Family, and Volunteering: Managing Demands Across the Triple Domains by Young Adults

2023· article· en· W4385218436 on OpenAlexaffabout
Uthpala Senarathne Tennakoon, Gabrielle Symbalisty

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsMount Royal University
Fundersnot available
KeywordsResource (disambiguation)PsychologyWork (physics)Domain (mathematical analysis)PrioritizationSocial psychologyApplied psychologySociologyComputer scienceBusinessProcess management

Abstract

fetched live from OpenAlex

This paper examines volunteer motivations and how individuals manage cross-domain demands of work, family, and volunteering, using twenty in-depth interviews with young adults aged 18-35 from Canada. By examining the interaction of all three domains, this study addresses a long-standing gap in the work-family literature in identifying the cross-domain implications of volunteering. The results revealed six distinct motivators for volunteering along the intrinsic-extrinsic motivation spectrum. These categories align with the volunteer functional inventory (VFI), providing external validation for VFI and connecting the functional analysis to the underlying motivations. This paper specifically adds to the literature by presenting the Matrix of Motivators and Resource Investment (MMRI), which identifies different volunteer types with the drivers for volunteering and resource prioritization in relation to work and family domains. Study also highlights practical implications and future research directions.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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 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
Published2023
Admission routes2
Has abstractyes

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