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Record W2595867944 · doi:10.1007/s11266-017-9857-x

The Effects of Volunteerism on Self-Deception and Locus of Control

2017· article· en· W2595867944 on OpenAlexaff
Naman Desai, Sharvari Dalal, Saurabh Rawal

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLocus of controlDeceptionPsychologySocial psychologyWelfareSelf-deceptionPublic relationsAutonomyBusinessPolitical science

Abstract

fetched live from OpenAlex

Abstract This study examines whether volunteering for not-for-profit Organizations (NPOs) which are involved in providing social welfare services and which actively promote sociobehavioral factors like social responsibility, leadership, and self-confidence among its volunteers, reduces an individual’s likelihood of engaging in corrupt practices. We identify two psychological traits: propensity to rationalize (as evidenced by self-deception) and an external locus of control (as compared to an internal LOC) that facilitate unethical behavior. With the help of volunteers from two NPOs, we investigate whether engaging in social welfare activities organized by such NPOs would create awareness about the adverse consequences of corruption faced by large segments of the society, which in turn would make it difficult to rationalize unethical and corrupt acts. Additionally, most NPOs actively strive to develop self-confidence and leadership skills among its volunteers. Prior literature indicates that individuals possessing such qualities are more likely to have an internal LOC and also that individuals possessing an internal LOC are less likely to act in a corrupt manner. The overall results indicate that greater experience with such NPOs leads to a significant reduction in propensity to rationalize and leads to a higher likelihood of having an internal LOC.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.004
GPT teacher head0.275
Teacher spread0.271 · 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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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