Funding Representation: An analysis of credit availability for minority owned businesses
Bibliographic record
Abstract
In the business world, a line of credit can be the deciding factor in a company's operation or closure. According to the Small Business Association, “small business credit cards account for $430 billion in spending, or about 1 in every 6 dollars spent on general purpose cards”, so clearly small time entrepreneurs, who specialize in taking monetary risks for profit, rely heavily on the ability to make purchases now and pay them off later. And in our modern world of start-ups, “mom and pop shops”, and online businesses, more and more people are looking to break into this scene. This includes marginalized and minority groups, those who traditionally have been denied or restricted from access to credit. In 2020, “an estimated 140,918 U.S. firms with majority Black or African American ownership, up 14% from 124,004 in 2017…” (Pew Research), while nearly “one in four new businesses is Hispanic owned” (SBA). My essay ventures to answer the question as to what policies have been enacted to work against these institutional blockades to credit access, how they've negatively and positively affected the longevity of minority owned businesses, and what changes can still be made to assist those who want to become self sufficient and make a more equitable field in the business world for all. My research pulls from numerous sources, including governmental journals, academic databases, and social action organizations dedicated to this very field.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".