Ideas for bridging the academic-policy divide at the nexus of gender and entrepreneurship
Bibliographic record
Abstract
Purpose Inspired by the “responsibility turn” in the broader organization/management literature, the overarching aim of this article is to help scholars working at the gender × entrepreneurship intersection produce research with a higher likelihood of being accessed, appreciated and acted upon by policy- practitioners. Consistent with this aim, we hope that our paper contributes to an increased use of academic-practitioner collaborations as a means of producing such research. Design/methodology/approach We selected Cunliffe and Pavlovich’s (2022) recently formulated “public organization/management studies” (public OMS) approach as our guiding methodology. We implemented this approach by forming a co-authorship team comprised of a policy professional and an entrepreneurship scholar and then engaging in a democratic, collaborative and mutually respectful process of knowledge cogeneration. Findings Our paper is comprised of four distinct sets of ideas. We start by describing who policy-practitioners are and what they want from academic research in general. We follow this with a comprehensive set of priorities for policy-oriented research at the gender × entrepreneurship nexus, accompanied by references to academic studies that offer initial insight into the identified priorities. We then offer suggestions for the separate and joint actions that scholars and policy-practitioners can take to increase policy-relevant research on gender and entrepreneurship. We end with a description and critical reflection on our application of the public OMS approach. Originality/value The ideas presented in our article offer an original response to recent work that has critiqued the policy implications (or lack thereof) within prior research at the gender × entrepreneurship nexus (Foss et al ., 2019). Our ideas also complement and extend existing recommendations for strengthening the practical contributions of academic scholarship at this intersection (Nelson, 2020). An especially unique aspect is our description of – and critical reflection upon – how we applied the public OMS approach to bridge the academic-policy divide.
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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 | 0.006 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".