Bibliographic record
Abstract
Kennedy Odede and Jessica Posner. Find Me Unafraid: Love, Loss, and Hope in an African Slum. New York: Ecco, October 2015. 336pp. 6X9. 16-page color insert. ISBN: 978-0-06-229285-8. $27.99.Theoretically, this could have become a great book that got to the heart of the various problems in Africa and how Americans can help local NGOs to solve them. But, instead, the authors have used it to write in a novelistic style, using two first-person voices. Instead of gradually and specifically explaining each of the problems and the steps they have taken to solve them, they wrote it like an action movie. The story opens from Kennedy Odede's slum in 2007. is hiding under the bed, amidst the rats, as military men are spraying bullets at the slum buildings, and killing his neighbors as they search for him. escapes, and the first thing he does is call a number on his cell phone. Then, the story moves back from December to September and is now from Jessica Posner's perspective. She is waiting for a call at a minibus station under the hot sun. Jessica stops to describe Daphne, tall, athletic and beautiful, with a Canadian father and Greek mother. She grew up traveling... (10). Across all these pages, if the reader has not read the back cover summary, he or she still has no clear idea regarding why Kennedy is hiding from the military or why Jessica is waiting for a bus in Africa. The information is given in small doses, as when Kennedy's best friend, Antony, comments, 'Kennedy knows how to make sure everyone feels like SHOFCO belongs to them.' Jessica then summarizes other things that Antony explained about Kennedy, He gives out small loans from his meager earnings, and then requires that instead of paying the loan back, the recipient designate a new person to receive a loan. The chain of loans has launched barbershops, water stands, vegetable stalls and many other small enterprises (19). Jessica overhears this information as the volunteers for SHOFCO. Since one of the major problems with African aide is that it frequently gets into the hands of corrupt politicians instead of reaching the poor, the narrator should realize at this point that a detail like these informal loans has to be fully explained. If a new loan is made to somebody else, then certainly the person has to repay the loan in order for the funds to go to somebody else-this sounds less charitable, but it's a more realistic funding model than if Kennedy keeps giving all of his hard-earned money away to a string of loans, none of which are ever repaid. …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".