Boundary Fluidity & Entrepreneurship: Space Between Insiders & Outsiders in Itinerant Communities
Bibliographic record
Abstract
This study extends theorizing on the impact of insiderness and outsiderness on entrepreneurial activity, its commercial outcomes, and the impact on individual entrepreneurs. We study the case of the self-marginalized canalboat community (a group of spatially mobile individuals living on boats in inland waterways in the United Kingdom) whose complex relationships with wider society defy simple insider-outsider dichotomies, and which we describe as 'boundary fluidity.' This group represents an intentional, alternative lifestyle challenging societal norms, particularly regarding home ownership, occupancy, and sedentariness. By investigating the power discrepancy between the self-marginalised and local governance on entrepreneurial growth or sustainability, we aim to extend the limited literature on self-marginalized entrepreneurship and to constructively contribute to recent interest in more nuanced forms of entrepreneurship. Focusing on the case of the canalboat community and utilising a multi-method qualitative research design, we find that for self-marginalised entrepreneurs, not only is entrepreneurship seen as an emancipatory tool, but the likelihood of facing challenges such as scalability constraints and resource scarcity is greatly increased in comparison to their mainstream counterparts. Moreover, unlike in other spaces of marginalized entrepreneurship, the boundaries between insiderness and outsiderness are particularly fluid, where entrepreneurs are willing to adjust their strategies to appeal to both insiders and outsiders, based on entrepreneurial goals.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".