The reason I jump : one boy's voice from the silence of autism
Bibliographic record
Abstract
The No. 1 Sunday Times and internationally bestselling account of life as a child with autism, now an award-winning documentary film. 'It will stretch your vision of what it is to be human' Andrew Solomon, The Times What is it like to have autism? How can we know what a person - especially a child - with autism is thinking and feeling? This groundbreaking book, written by Naoki Higashida when he was only thirteen, provides some answers. Severely autistic and non-verbal, Naoki learnt to communicate by using a 'cardboard keyboard' - and what he has to say gives a rare insight into an autistically-wired mind. He explains behaviour he's aware can be baffling such as why he likes to jump and why some people with autism dislike being touched; he describes how he perceives and navigates the world, sharing his thoughts and feelings about time, life, beauty and nature; and he offers an unforgettable short story. Proving that people with autism do not lack imagination, humour or empathy, THE REASON I JUMP made a major impact on its publication in English. Widely praised, it was an immediate No. 1 Sunday Times bestseller as well as a New York Times bestseller and has since been published in over thirty languages. In 2020, a documentary film based on the book received its world premiere at the Sundance Film Festival. Directed by Jerry Rothwell, produced by Jeremy Dear, Stevie Lee and Al Morrow, and funded by Vulcan Productions and the British Film Institute, it won the festival's Audience Award for World Cinema Documentary, then further awards at the Vancouver, Denver and Valladolid International Film Festivals before its global release in 2021. The book includes eleven original illustrations inspired by Naoki's words, by the artistic duo Kai and Sunny.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".