Pushed or Pulled? Women's Entry into Self‐Employment and Small Business Ownership
Bibliographic record
Abstract
Over the past two decades the economies of Canada and many other industrialized countries have seen significant restructuring, bringing with it steadily rising levels of self‐employment and small business ownership. Women have been at the forefront of this change. Of the many questions raised by women's entrance into self‐employment, a central one concerns the factors fuelling its growth. While some argue that women have been pulled into self‐employment by the promise of independence, flexibility and the opportunity to escape barriers in paid employment, others argue that women have been pushed into it as restructuring and downsizing has eroded the availability of once secure jobs in the public and private sector. To date, existing research on the ‘push–pull’ debate has not fully answered; these questions, with survey and labour force data suggesting only general and sometimes contradictory, trends. This article examines this issue in greater detail, drawing on in‐depth interviews with 61 self‐employed women in Canada. Overall their experiences shed further light on the expansion of women's self‐employment in the 1990s, suggesting push factors have been underestimated and challenging the current contours of the ‘push–pull’ debate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".