Bibliographic record
Abstract
Steven Pinker (2018, page 40) reports that “… large majorities in eleven developed countries said ‘the world is getting worse’ and in most of the last forty years a solid majority of Americans have said the country is ‘heading in the wrong direction.’” Recent polls conducted by the Pew Research Center show that Americans are pessimistic about the future (Gramlich, 2019) and that around the world many people are dissatisfied with the way democracy works in their countries (Wike et al., 2019). According to Twenge (2019), the level of happiness in the United States has been trending down for the past twenty years. Is all this gloom really justified? It is true that humanity faces grave challenges such as climate change and rising economic inequality and that basic human rights are violated every day in countries around the world. But if one steps back and takes a somewhat longer view of history, a case can be made that we’ve never had it so good. The data in Table 1 show that life expectancy at birth has more than doubled in many countries from between 25 and 40 years in 1800 to between 70 and 84 years in 2016. Increases in real (inflation-adjusted) per capita annual income have been even more dramatic rising from around $2,000 per person per year in 1820 in the United States and United Kingdom to $53,015 and $39,162 respectively in 2016. In Norway, real per capita income increased from $1,330 in 1820 to $76,397 in 2016 and real per capita income in South Korea is 75 times greater today than it was in 1820, 42 times greater than in 1920.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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".