Heavy-Work Investment: Its dimensionality, Invariance across 9 Countries and Levels before and during the COVID-19’s Pandemic
Bibliographic record
Abstract
The goals of the current comparative and half-exploratory paper are to: 1) shed light on the properties of the relatively “new” construct, Heavy-Work Investment (HWI) and its two dimensions – Time Commitment and Work Intensity, (2) assess differences across 9 countries in relation to HWI, (3) gauge the effect of demographical parameters on HWI, and (4) investigate the interaction between them and COVID-19’s pandemic (i.e., before COVID-19, and during the COVID-19 pandemic). Data of 3,418 employees were collected from 9 different countries: Israel, Romania, Japan, USA, Pakistan, Italy, Turkey, Brazil, and Germany. Among other findings, analyses revealed that HWI construct is stable across countries and that the mean investment at work (in the form of both time and efforts) is higher during the COVID-19’s pandemic than before it. Discussion section summarizes the findings of the entire research, and elaborates on limitations and future research suggestions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".