Bibliographic record
Abstract
African Americans and Latino Americans have played an increasingly significant role in the ongoing saga of American sports—and not just in popular sports like basketball and baseball. This is the first comprehensive, multisport biographical resource to concentrate exclusively on the accomplishments, achievements, and personal struggles of notable African American and Latino American athletes of the last quarter century. A total of 175 important contemporary athletes—113 African Americans and 62 Latino Americans—are profiled. Most made significant contributions to their sport since 1990. Athletes include Roberto Alomar, Oscar De La Hoya, Forence Griffith Joyner, Evander Holyfield, Michael Johnson, Michael Jordan, Jackie Joyner-Kersee, Ray Lewis, Sammy Sosa, Serena and Venus Williams, Tiger Woods, and many more. Eighteen sports, from baseball to bobsledding, are covered. The profiles of the men and women include personal background information and athletic career achievements through 2002. Each athletic career is traced, including entrance into sport, major accomplishments, records set, awards and honors, and overall impact. Quotations from the athletes enrich each profile. Bibliographies and photos complement the entries.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.027 |
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".