New and small firms in a modern working life: how do we make entrepreneurship healthy?
Bibliographic record
Abstract
Abstract The interplay between health, entrepreneurship and small and emerging businesses is a research field receiving growing interest. Studies point to both health-related risks and opportunities, which have implications for the social and economic lives of entrepreneurs and employees in small and new firms. Research has been carried out in different disciplines, which have contributed in different ways to the understanding of this inquiry. As the field is still premature and interdisciplinary in nature, there is a need to establish boundary-crossing avenues for developing new knowledge on the topic. This ambition has led to the development of this special issue. The issue includes results from original research on working life challenges encountered by small and new businesses, approached from a variety of disciplines. In this introduction, we begin by tracing an overarching framework, to which we add brief descriptions of the contributing papers. To conclude, we outline future research goals and discuss how issues around mental health, regulation and work environment inspections, race, disability and gender issues and the growing gig economy will affect the conditions for healthy entrepreneurial work.
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 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) | 0.001 | 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".