Meaningfulness in Work in Brazilian and French Creative Industries
Bibliographic record
Abstract
Abstract This study aimed to investigate the meaningfulness that Brazilian and French artists find in their work, considering the historic French cultural influence in the Brazilian creative industry. The specific objective was to cross-culturally validate a model of meaningfulness in work that was developed in French Canada and that includes five latent variables: learning and development, utility of work, quality of working relationships, autonomy, and moral correctness. The present study used a French Canadian measurement instrument that was developed for the health care and management occupations in Quebec. A total of 648 individuals, 280 in France and 368 in Brazil, provided online responses that were then analyzed using Confirmatory Factor Analysis (CFA) and Multigroup Confirmatory Factor Analysis (MGCFA). The five–factor structure of the meaningfulness in work model was found to be similar for the two samples of artists—although this model was a better fit to the data for the Brazilian creative professionals than the data for their French counterparts. The analyses showed that the two groups understand the structure of the meaningfulness factors in a similar manner (configural and metric invariance). The study also showed that conceived as a social and economic core activity, work is present in the context of the arts as well as in the traditional sectors of the economy for which the model was developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".