Bibliographic record
Abstract
None of us can really remember anything about our lives before the age of two years. How much of what makes us what we are has been set by that time? We challenge the widely-accepted idea that what we are is ‘determined’ by inherited genes and we start to explore how interaction with parents/carers establishes our behaviour. We use examples drawn from fiction and the real world to explore how the brain learns from the conditions in early life. We explain why this adaptability underpins development of our senses, our behaviour and our self-control. This introduces control as one of the themes of the book – how much we are in control of our bodies and how control develops based on environmental cues. We question what effect today’s exposure to digital media may have on the developing brain, and explore new ideas about the development of defence mechanisms, from immunity to the gut microbiome. Through the quote from JM Barrie, author of ‘Peter Pan’: ‘You always know after you are two. Two is the beginning of the end’, we ask whether age two is the beginning of the end or the end of the beginning of development.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".