Heavy Work Investment from the Perspective of Cultural Factors and Outcomes by Types of Investors
Bibliographic record
Abstract
This paper aims to analyse the concept of Heavy Work Investment (HWI) by studying the factors determining this phenomenon, as well as its outcomes (both negative and positive). According to the European Directive of 1993, Heavy Work Investment occurs when an individual works more than 48 hours per week. The aim of this paper is to study the factors influencing the occurrence of the phenomenon of Heavy Work Investment from the perspective of time invested, using the multiple regression model, as well as the outcomes of Heavy Work Investment, using the structural equation model (SEM). The study used the data of the countries included in the International Social Survey Programme (ISSP), (37 countries and a total of 18,274 respondents) on employment status, the number of actual working hours, job and demographic characteristics. The results confirm the important impact of the cross-cultural differences on HWI behaviour as well as the outcomes of Heavy Work Investment according to the type of investor (dispositional / situational).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".