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Record W2920901152 · doi:10.1080/14927713.2019.1582355

Exploring dimensions of satisfaction experienced by student volunteers

2019· article· en· W2920901152 on OpenAlexaffvenueabout
Alexander Yuriev

Bibliographic record

VenueLeisure/Loisir · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychologySocial psychologyTurnoverProfit (economics)ExternalityManagementEconomics

Abstract

fetched live from OpenAlex

The significance of involving young volunteers in various projects, notably students of colleges and universities, cannot be underestimated: such global events as Olympics or Soccer World Cup would hardly take place without them. However, non-profit organizations find it challenging to retain such volunteers, as the majority of students take part in social campaigns only occasionally. This research employs a complex qualitative methodology to investigate a specific case of a student volunteer group in one of Canada’s universities with a relatively low turnover rate. It is suggested that the satisfaction obtained during fulfilling voluntary tasks defines the willingness and the likelihood of students to stay in a group. The analysis of collected data revealed four principal emotions associated with the satisfaction of young volunteers: sense of freedom, sense of belonging, hope of positive externalities, and sense of fulfillment. Several managerial and theoretical contributions conclude this paper.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.055
GPT teacher head0.319
Teacher spread0.264 · 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

Citations3
Published2019
Admission routes3
Has abstractyes

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