Bibliographic record
Abstract
At the 1997 Microcredit Summit in Washington DC, the primary goal of microfinance agreed to was that it was a tool to upset conventional banking and to make financial services inclusive; yet this is not the case for microfinance in Guyana. Educated Indo-Guyanese lenders admitted to their own personal biases against Afro-Guyanese as clients without remorse in this case study of 93 people. I argue in this paper that micro-banking managers and staff hold onto historically-rooted prejudices which interfere with the allocation of loans. Race is seemingly the issue that divides the populace; however, I found issues of racial, class and gender bias intertwined in the lending process that deny poor Afro-Guyanese women loans. Personal bias can affect the management of economic development pro-grammes for the poor and it should not be assumed that microfinance helps everyone.Resumen: La exclusión de los ‘hucksters’ afroguyaneses en la microbancaEn la Cumbre de Microcrédito celebrada en Washington DC en 1997, se acordó que el objetivo principal de las microfinanzas era servir como herramienta para desbaratar la banca convencional y hacer inclusivos los servicios financieros; sin embargo, éste no es el caso de las microfinanzas en Guyana. Prestamistas indoguyaneses con una buena formación se deja-ron llevar sin reparos por sus propios prejuicios personales contra los afroguyaneses como clientes en este estudio de caso de 93 personas. En este documento sostengo que los directores y el personal de la microbanca se aferran a prejuicios enraizados históricamente que interfieren en la concesión de préstamos. Aparentemente la raza es la cuestión que divide al pueblo; sin embargo, identifiqué prejuicios raciales, de clase y de género entrelazados en el proceso crediticio por los que se deniegan préstamos a mujeres afroguyanesas pobres. Los prejuicios personales pueden afectar a la gestión de los programas de desarrollo económico para los pobres y no se debe asumir que las microfinanzas ayudan a todo el mundo.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".