Bibliographic record
Abstract
Hadfield, C., & K. Fillion (2016). The Darkest Dark, illustrated by T. Fan and E. Fan. Tundra Books, 2016.The Darkest Dark takes us into the adventures of young Chris the astronaut. Together with his dog Albert, Chris is busy exploring Mars and saving the planet from aliens. Chris is so busy being an astronaut that he even has trouble sleeping at night, when the dark corners of his bedroom reveal aliens and monsters of all kinds. Chris’ parents are a bit dismayed that Chris cannot sleep in his own bed and they try a number of tricks to teach Chris that his bedroom is perfectly safe from aliens. Then on a July evening Chris and his family go to the neighbours to watch the most spectacular event: men landing on the moon. Witnessing this modern miracle Chris is so inspired that he finds himself changed, and no longer afraid of the dark. This touching story is based on the childhood of astronaut Chris Hadfield, the first Canadian to walk in space. Hadfield and Kate Fillion have captured a story that is simple in many respects, however singularly inspiring, as it shows how Chris grows to overcome his fears and to realize his dreams. The graphite illustrations by Terry and Eric Fan are remarkably rich in detail. The subtle colours and contrasting dark tones bring a young child’s imagination to life. The imagery of the Hadfield cabin on Stag Island at times seems so real you can almost hear the water lapping and the crickets in the distance. This would be an excellent book for young children between the ages of 4-12, especially those interested in space or experiencing fear of the dark. Parents can appreciate the lovely illustrations as well as the historical photos and factual information at the end of the book.Reviewer: Hanne PearceHighly recommended: 4 out of 4 starsHanne Pearce has worked at the University of Alberta Libraries since 2004. She has continuing interests in writing, photography and graphic design. She is currently working towards completing a Masters in Communications and Technology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.122 | 0.055 |
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".