Bibliographic record
Abstract
The following are excerpts from the Washington Post over the period of a few months: Gates loses $12,000,000,000 (billion) in stock in week: Still world's richest person. Eight million American Households have net worth in excess of $1,000,000. Gap between rich and poor in America is growing. In affluent latte communities people pay more than $2.00 for designer coffee and water. Three billion people in world survive on less than $2.00 per day. Adolescent obesity is a growing (sic) problem in affluent countries. More than 250,000,000 children in world suffer from malnutrition. Top National Football League draft choices to receive around $10,000,000 signing bonuses. Janitors in Los Angeles strike for raise to $8.00 an hour. Almost 75% of HIV/AIDS cases occur in sub-Sahara Africa. African leaders accuse North American and European pharmaceutical companies of inflating cost of medicine. The companies respond that the real problem is inadequate distribution systems in the countries. Four European executives agree to jail terms for attempting to control cost of vitamins worldwide. Protesters demand debt forgiveness for third world countries at World Bank and International Monetary Fund meetings. Leaders of G-7 countries propose study of the digital divide between affluent and poor countries. Leaders of third world countries skeptical. Each of us could go on and on with examples of the paradox of the growing gaps between the haves and the have-nots across countries and even within each country. At a national level, it is difficult to commit adequate resources to education if large numbers of a population are suffering from malnutrition, disease, and poverty. This is exacerbated by military conflicts and the attendant human suffering. Even in the United States, Canada, and countries of Western Europe, where there has been unprecedented prosperity and the threat of military conflict is small, there is a disparity in the quality of life. This is especially apparent in countries with large, often poor, immigrant populations such as Germany, Canada, the United States, and Switzerland. Those of us who were born in the United States are aware that our parents, grandparents, or more distant ancestors mostly came from impoverished backgrounds and took on menial, backbreaking work to survive. We are less aware that schools in the first half of the last century tried to forcefeed children from Italian, Yiddish, and Polish-speaking families, for example, a distorted, parochial, and punitive version of Americanization. The situation is only marginally better today. …
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.510 | 0.488 |
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".