Bibliographic record
Abstract
Abstract It is now more than two years since COVID-19 spread around the world and the World Health Organization declared a global pandemic. The pandemic raised important questions about what work would look like after the pandemic, evidenced by the great debate on the future of remote work. Given how inaccurate predictions of the future often turn out to be, a more important question is, what should work look like if we are to achieve productive, healthy, and safe work? Fortunately, research by scholars such as Marie Jahoda, Abe Maslow, Fred Herzberg, Richard Hackman and Greg Oldham, Robert Karasek and Töres Theorell, Michael Marmot, and Peter Warr, spanning almost a century, provides answers. Fulfilling people’s needs for the seven interrelated characteristics, namely quality leadership, autonomy, belonging, fairness, growth, meaning, and safety, are the keys to productive, healthy, and safe work, and we devote a separate chapters to each of these topics. Each chapter discusses how the seven interrelated characteristics were affected by the pandemic, and how gender affects how people experience these dimensions. A central idea of the book is that small changes make a big difference in the long term, perhaps especially during the most trying times, and that small changes in the seven characteristics are enough to achieve productive, healthy, and safe work, with effective, evidence-based interventions presented to document this. Underlying each topic is the idea that we can achieve more by changing work than by trying to change people.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.081 | 0.024 |
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".