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Record W4380520769 · doi:10.6000/1929-4409.2020.09.39

How Self Control and Situational Pressure Influence the Tendency to Receive Gratification: An Experimental Study

2022· article· en· W4380520769 on OpenAlexvenueno aff
Dodik Ariyanto, Gilang Maulana Firdaus, Maria Mediatrix Ratna Sari, Ayu Aryista Dewi, I Made Gilang Jhuniantara

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSituational ethicsSocial psychologyPsychologyControl (management)Affect (linguistics)GratificationVariance (accounting)Empirical researchManagementBusinessStatisticsMathematicsEconomics

Abstract

fetched live from OpenAlex

This study aimed to produce empirical evidence on tendency differences to accept gratuities between individuals with a high level of self-control and a low level of self-control, the conditions are the presence or absence of situational pressure. The method used is an experiment with a 2x2 factorial design. A total of 136 officers and staff in the Directorate General of State Bali region become research participants. The data were processed with statistical parametric, two-way ANOVA. The results showed that individuals with high levels of self-control have a lower tendency to accept gratuities than participants with lower levels of self-control. However, this study did not obtain empirical evidence indicating situational pressures experienced by the individual can affect the tendency to accept gratuities. Interaction hypothesis testing showed that the interaction between the individual levels of self-control and situational pressures experienced affects the tendency to accept gratuities.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.065
GPT teacher head0.303
Teacher spread0.238 · 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 designBench or experimental
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

Citations5
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Criminology and SociologySame topicTaxation and Compliance StudiesFrench-language works237,207