Bibliographic record
Abstract
Soft Skills - A survey looking at work/life balance in the 21st century. Work to live, or live to work? Working hard, getting the job done, and having a fulfilled life at home—can we really have it all? In a globalized world where business never stops and nearly all our business tools enable constant communication, where do we draw the line between work and leisure? In addition, what impact does separation between work and leisure have on our performance, job satisfaction, and wellbeing? These intriguing questions were among those posed to participants in The Way Ahead’s global survey in an attempt to investigate and contrast work/life balances found in the oil and gas industry compared with other industries. The Concept of Work/Life Balance Work/life balance is a concept that significantly affects the health and happiness of one’s life. The concept rests on your agenda for demarcating the amount of time you spend with work and the amount of time you spend in leisurely pursuits, where leisure is everything outside the scope of your work. Work/life balance is an area of research dating back to the 1960s. In fact, it began as a topic of study in management as an attempt to formulate working conditions that maximized productivity for industrial companies. It has often been a theme of political and social discussion, and it is important to realize that different societies have different perceptions about what a suitable work/life balance is.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".