Bibliographic record
Abstract
Microcredit - the extension of small loans - gives people who would otherwise not have access to credit the opportunity to begin or expand businesses or to pursue job-specific training. These borrowers lack the income, credit history, assets, or security to borrow from other sources. Although the popularity and success of microcredit in developing countries has been trumpeted in the media, microcredit is established and growing in the United States and Canada as well. Its appeal comes from its capacity to provide the means for those who have the ability, drive, and commitment to overcome the hurdles to self-sufficiency. In this article, the role of microcredit as a stimulant for economic development is examined. First, its importance for the establishment of small businesss is described. Second, the article provides an overview of the general microcredit climate in the United states and the local situation in the Ottawa area. Third, brief stories about individuals who have received this type of loan reveal the human impact behind the economic benefits. Finally, the role of microcredit in funding startups is analyzed in comparison to other sources of available funding. The article concludes with a summary of the benefits of microcredit as a win-win proposition for economic development.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".