Bibliographic record
Abstract
What is it like to go blind? 350 million people around the world live with severe vision impairment, ranging from those who can see a couple of letters on a sight chart to those who perceive no light at all. In this book we meet some of them, including artists, poets, scientists, architects, politicians, broadcasters and musicians. Together, we discuss every stage of life with vision impairment – from childhood and education to dating, employment and ageing – as well as the portrayal of blind people in literature and film, the use of technology by people with vision impairment, and the psychological effects of losing vision. Vision Impairment also reviews the major causes of sight loss today and shows the effect of these diseases on visual function. It surveys new and emerging treatments for serious eye diseases and explores what it is like to have vision restored after decades of being blind. Based on Michael Crossland’s extensive work in children’s and adults’ low vision clinics, and his 20 years of research into vision impairment, the book blends individual stories, key research findings and the most recent scientific discoveries to present an informative yet optimistic overview of living with sight loss. Praise for Vision Impairment ‘The key strength of this book is how scientific concepts around ophthalmology, vision science and sight loss are brought to life by letting patients explain what these concepts mean to them. This is not a book that has been written “about” patients; it has been written “with” them. In this sense, it is very much original, and I enjoyed reading it tremendously.’ Keziah Latham, Anglia Ruskin University
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 | 0.004 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.231 | 0.117 |
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".