Feminist attributes and entrepreneurial identity
Bibliographic record
Abstract
Purpose The purpose of this study is to examine how feminist attributes are expressed within entrepreneurial identity. Design/methodology/approach The study employed a purposive sampling technique to recruit 15 self‐identified “feminist entrepreneurs”. This included retailers, manufacturers, exploration operators, consultants, and professionals. Qualitative data were subject to content analysis. Findings Contrary to a feminine archetype portrayed as caring and nurturing, respondents do not describe themselves as typically portrayed in the feminist literature. Prevalent themes included participative leadership, action‐oriented, and creative thinker/or problem solver. Research limitations/implications Researchers should use caution in assuming feminist discourse has direct application to characterizing or stereotyping “feminist” entrepreneurs. The applicability and reliability of “off the shelf” psychometrics to describe contemporary gender roles across the myriads of processes associated with venture creation must also be questioned. Limitations: the purposive and small‐sample limits the generalizability of findings to the diverse community of female entrepreneurs. Testing of the applicability, validity, and reliability of the nomenclature used to describe self‐identity is warranted across international samples of feminist entrepreneurs. Practical implications The current study provides an inventory of feminist entrepreneurs' self‐described leadership attributes. The nomenclature can be used by women‐focused trainers to help clients to recognize their entrepreneurial attributes. Social implications The study may assist women in recognizing identity synergies and conflicts (e.g. within themselves and among family, employees, clients, etc.). Originality/value This is the first study that documents feminist entrepreneurs' leadership attributes. As such, the work is a step in seeking to reconcile feminist theory and entrepreneurial practice.
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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 | 0.001 | 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 | 0.000 |
| 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".