The "dying child's wish" complex: the case of the Craig Shergold Appeal
Bibliographic record
Abstract
The ''Dying Child's Wish'' complex of narratives, beliefs and practises focuses on a little boy who is purported to be dying from a terminal illness.His wish is to have his name in the Guinness Book of Records for having collected the greatest number of get-well cards (or alternatively postcards, Christmas cards, stamps, or hats or other such items).In response, individuals are asked to send cards, gifts, money, or words of encouragement, and to organize collection projects to help the child.People are frequently so taken with the substance of the stoty that they also encourage others to respond to this worthwhile appeal: Craig Shergold of Surrey is seven years old and is dying from plural tmnours of the brain and spine.He has one ambition to fulfil and that is to gain an entry in the Guinness Book of Records as the recipient of the greatest number of getwell cards.He will need 1;)300;)000 cards in order to achieve this.IfDorset schoolchildren would get involved with this simple but worthwhile cause, Craig would be well on the way to breaking the record.Any schools or organisations willing to participate or who would like to know more details please contact Jane Regan at the Dorset Science & Technology Centr[e] te: 0929 405063 (Dorset County Council Education Circular 1989).There are also a number of stories in circulation which suggest that these appeals to help dying children are just rumours, nothing but legends, hoaxes, or a mixture of fact and fiction:
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.062 | 0.033 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".