Economic Disparities Among Small Business Owners in Small-Town America
Bibliographic record
Abstract
The land of equal opportunity," a minority entrepreneur describes what she had always pictured the United States to be.After struggling to obtain a business loan, she says that her business is finally starting to see some success.Hundreds of thousands of entrepreneurs have similar dreams of owning a successful business, but over 60% of female and minority small business owners struggle to do so, per a 2023 Bankrate report [2].In this study, 25 small businesses in Hillsborough, New Jersey, responded to a survey confirming similar national trends among entrepreneurs.These results, representative of 25% of Hillsborough's small businesses, indicated that female and minority-owned businesses face greater difficulty obtaining financial assistance and managing their business along with personal responsibilities.Furthermore, Hillsborough was chosen due to its upper-middle-class characteristics, thus making the study's results applicable across similar towns in the United States.The paper concludes by analyzing potential solutions to these disparities. Key TermsEconomics: Study of how resources are allocated and managed among small businesses, specifically focusing on the financial challenges faced by female and minority entrepreneurs in Hillsborough, New Jersey, and their implications on access to loans, business outcomes, and overall economic disparities.Small Business: Businesses with gross revenues that do not exceed $3 million.Minority: A person whose race or ethnicity is a non-dominant race within the group.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".