Bibliographic record
Abstract
Purpose Most feminists policies are aspirational. Deficiencies include vague terms of what constitutes ‘feminist’ within policy, ambiguous investment criteria, lack of consultation and the use of the binary definition of gender negating gender-diverse people (Tiessen, 2019). The purpose of this study is to identify parameters that characterize feminist entrepreneurship policies and to advance recommendations to operationalize these policies. Design/methodology/approach The COVID-19 pandemic has unveiled fragilities in the socio-economic gains that women entrepreneurs have achieved. Gender-regression is, in part, the product of entrepreneurship policies that fail to recognize the nature and needs of women entrepreneurs. To inform recovery measures, this article considers two research questions: what are the parameters of feminist entrepreneurship policies? and how can parameters of feminist entrepreneurship policy be operationalized in pandemic recovery measures? To inform the questions, the study draws on the academic literature and thematic analysis of three collective feminist action plans to operationalize ten parameters that characterize feminist entrepreneurship policy. Findings Supplanting ‘feminist’ for women in the construction of entrepreneurship policies, without specifications of how parameters differ dilutes government's efforts to achieve gender quality and women's economic empowerment. To inform policy, recommendations of three feminist recovery policies clustered under seven themes: importance of addressing root causes of inequality; need to invest in social and economic outcomes; economic security; enhancing access to economic resources; investment in infrastructure; inclusive decision-making; and need for gender disaggregated data to inform policy. Differences in policy priorities between collective feminist recovery plans and the academic literature are reported. Research limitations/implications The parameters of feminist entrepreneurial policy require further interpretation and adaptation in different policy, cultural and geo-political contexts. Scholarly attention might focus on advisory processes that inform feminist policies, such as measures to address gender-regressive impacts of the COVID-19 pandemic. Research is also needed to understand the impacts of feminist policies on the lived experiences of diverse women entrepreneurs. Limitations: The study design did not incorporate viewpoints of policymakers or capture bureaucratic boundary patrolling practices that stymie feminist policies. Thematic analysis was limited to three feminist recovery plans from two countries. Practical implications Recommendations to operationalize feminist entrepreneurship policies in the context of pandemic recovery are described. Originality/value Ten parameters of feminist entrepreneurship policy are explored. The conceptual study also advances a framework of feminist entrepreneurship policy and considers boundary conditions for when and how the parameters are applicable.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".