Time affluence, material affluence and work experiences of professional women in Russia
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the role of time affluence (TA) and material affluence (MA) in work and extra-work experiences of a sample of professional women working in Russia. Design/methodology/approach – Data were collected form 168 women using anonymously completed questionnaires. Measures included personal demographic and work situation characteristics, work outcomes, indicators of work investment and extra-work outcomes. Findings – TA and MA were significantly and positively correlated (r=0.22), with women indicating similar levels of TA and MA. Women reporting higher levels of MA generally indicated more favorable work outcomes (higher job satisfaction, lower intent to quit). Women reporting lower levels of TA generally indicated higher levels of work investment. However, neither TA nor MA predicted family satisfaction. Research limitations/implications – This study highlights the importance to take steps to increase TA and MA of Russian women to positively influence their work and extra-work experiences. Originality/value – These findings replicate and extend earlier USA, Egyptian and Turkish results to Russia. Unlike previous studies, the authors simultaneously included TA and MA constructs, thus providing important comparisons of their relationships with different outcomes. The authors also respond to the call to study TA and MA in different cultural contexts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".