Cluster Analysis and Population Density Theories: An Exploratory Study of Indigenous Female Entrepreneurship
Bibliographic record
Abstract
The last twenty years have seen significant contributions in the literature on women entrepreneurship. This has been augmented with studies that are engaged in comparative analyses across gender, borders, and industries. While these studies provide insights into the status of women entrepreneurs, what is often missing and what has not emerged are niche studies that look at women entrepreneurs from a particular group, a particular ethnicity, a particular genre - going deep in any of these directions, with rigorous statistical analyses while focusing on and deriving conclusions from said in-depth focus. In essence, studies asking questions that call for comprehensive, multiple layer analyses with statistically backed answers that produce substantial conclusions in terms of their contribution to the forum of women entrepreneurs in general and specific niche groups of women entrepreneurs in particular. With this in mind, we turn our attention to the world of Native American entrepreneurs and specifically Native American female-owned enterprises and examine these questions as we consider the context on female entrepreneurship from the setting of the Indigenous community.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".