Bibliographic record
Abstract
Our world is awash in preventable and undeserved misery.The World Bank Group (Cruz et al. 2015) estimates that currently about 9.6% of the global population or 702 million people live in extreme poverty or on less than US $1.90 per day.The extremely poor are unable to meet their most basic needs for nutrition, medical care, and shelter.As a result, they suffer and/or die from preventable illness and disease and malnutrition.Sub-Saharan Africa is particularly hard hit: it is estimated that about 35% of its population is extremely poor (Cruz et al. 2015).Unsurprisingly, this region has the highest under-five mortality rate on the planet.The extremely poor are not the only ones living wretched lives.Each year vast numbers of non-human animals suffer and are killed in order to produce inexpensive, palatable foodstuffs.Billions of broiler chickens, for instance, are kept in cramped, polluted conditions, unable to engage in species-specific behavior, causing them to suffer from disease and to experience debilitating stress.The fact that they are bred to gain weight quickly only adds to their misery; their bone structure is often unable to support their bulk, leading to disability and deformity.At the end of their short lives, such chickens are deprived of food, captured, shipped in horrid conditions to slaughterhouses, and then killed (often painfully and brutally).The misery produced by extreme poverty and by our treatment of non-human animals calls for some kind of practical response.Much of this suffering and premature death is, after all, preventable, often easily so.There are, however, deep divisions over how best to respond in practice.One response to extreme poverty involves advocating for donations (or provisions of labor) to philanthropic organizations dedicated to preventing premature death and/or to preventing or alleviating suffering due to it.In the case of non-human animals, one response is to avoid consuming them and the products derived from them, especially those produced in factory farms, where conditions are inordinately despicable, and to donate (or labor for) charities aiming to improve the plight of non-human animals.This raises a number of moral questions.Should one respond in this way?How much should one contribute to philanthropic organizations, if in fact one should do so?How should one give?Through which conduits should one direct one's contributions?Peter Singer has devoted his career in part to dealing with these questions.His work (1972, 1993) on what the global wealthy ought to do in response to extreme poverty
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.241 | 0.144 |
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".