Bibliographic record
Abstract
In this Special Issue on Gender and Entrepreneurship, this subject is examined from multiple perspectives.Like a diamond, the topic is multi-faceted and requires a holistic treatment if we are even to begin to understand its complexity and its value.The work curated here is constituted of two invited editorials, an invited research paper and six competitive research articles, representing the scholarly efforts of respected researchers in this field, established and emerging.It is hoped that this collection will inspire still more scholarship in this important and growing area of entrepreneurship research.The Special Issue begins with two invited editorials that set the context and tone for this collection of work.Mustafa and Treanor provide us with insights into the evolution of the field of gender and entrepreneurship, with a focus on the importance of context.They call for the incorporation of gender considerations in all future entrepreneurship research.Jennings and Tonoyan explore the potential for further increasing our knowledge regarding gender stereotyping in the entrepreneurship context.They suggest three "paths" such research might take, providing a theoretical foundation for each and offering questions that might be investigated.Both of these editorials advocate for more of the type of research pursued in the invited and competitive articles found in this issue.The invited research paper by Henry et al. delves into policy and practice as it pertains to the financing of women-owned entrepreneurial ventures.Using the countries of Canada, Ireland and the US as context, they apply institutional theory to detect gaps in policy and practice and draw international comparisons.In doing so, they highlight the importance of context in shaping policy regarding the financing of women's entrepreneurial ventures, amplifying the observations of Mustafa and Treanor in their editorial.The essence of the competitive research articles is captured very effectively in the table found in Mustafa and Treanor's editorial.These papers explore a diversity of topics within the field of gender and entrepreneurship including
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".