Bibliographic record
Abstract
The helplessness of newborn babies is very endearing. They can just about breathe unaided, but they are otherwise entirely unadapted and dependent. Babies can barely see, let alone walk or talk. Few animals come into the world so unprepared, and no other species is as dependent on learning as human beings are. Elephant calves, for instance, can stand up by themselves within a few minutes of being born. Most animals are similarly “preprogrammed.” Female elephants carry their young for no fewer than 22 months, whereas we humans have to go on investing in our offspring long after they are born. Children need years of adult protection. They guzzle fuel, too; their brains consume fully 60 percent of the newborn’s total energy intake. In the first year of life, the infant’s head buzzes with activity as neurons grow in size and complexity and form their innumerable interconnections. The way the brain develops is the subject of the next chapter (chapter 5.2). Here we concentrate on the way we are educated from the first day on. There is virtually no difference between Inuits and Australian aborigines in terms of their ability—at opposite ends of the earth and in climates that are utterly different—to bear children successfully. Other animal species are far more closely interrelated with their environment. Other primates have evolved to occupy a limited biotope determined by food and climate. Humans are much more universal. Every human child has an equal chance of survival wherever they are born. As a species, we delay our maturation and adaptation until after birth, which makes the inequality of subsequent human development all the more acute. Someone who is born in Mali or Burkina Faso is unlikely ever to learn to read. A person whose father lives in Oxford, by contrast, might have spoken his or her first words of Latin at an early age. Inuit and aboriginal babies may be born equally, but their chances begin to diverge the moment they start learning how to live. We are not shaped by our inborn nature but by the culture that is impressed upon us by the people with whom we grow up.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.530 | 0.294 |
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".