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Record W4402526675 · doi:10.5539/jel.v14n1p27

Pleasure, Suffering, and Engagement: Behavioral Triggers in Brazilian Research Scholarship Students

2024· article· en· W4402526675 on OpenAlexvenueno aff
Luís Felipe Dias Lopes, Adriane Fabrício, Lucas Charão Brito, Deoclécio Júnior Cardoso da Silva, Estéfana da Silva Stertz, Giovanna Buzanello de Vargas, Vanessa Hasper Dessbessell

Bibliographic record

VenueJournal of Education and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPleasurePsychologyScholarshipStudent engagementSocial psychologyPedagogyPsychotherapist

Abstract

fetched live from OpenAlex

This study aimed to investigate the relationship between pleasure, suffering, and work engagement, along with its dimensions, in the professional context of postgraduate scholarship students in Brazil. The study was conducted with 1,027 scholarship recipients from different states in Brazil. It utilized theoretical frameworks from the Psychodynamics of Work, primarily based on the studies of Christophe Dejours, to analyze the subjective experiences of pleasure and suffering at work. The results indicated that freedom of expression negatively influences students’ vigor and dedication. Professional fulfillment impacts vigor, dedication, and absorption positively. Professional exhaustion adversely affects vigor, and unexpectedly, the lack of recognition impacts the vigor of the students positively. The study concludes that emotional factors in the workplace, such as the ability to express emotions, professional fulfillment, emotional exhaustion, and recognition, significantly affect key aspects of work engagement among postgraduate scholarship students.

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)0.007
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.003
Research integrity0.0010.001
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 head0.160
GPT teacher head0.567
Teacher spread0.407 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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