Bibliographic record
Abstract
Sheen, Barbara. Artificial Eyes. Norwood House Press, 2017.Artificial Eyes is one of a series of non-fiction books called Tech Bytes, that “explores...new technologies and how they are changing the way people perform everyday tasks.” Barbara Sheen, author of almost 100 children’s books, explores the history of artificial eyes, how they are made, their effect on people’s lives, and future developments. This is a detailed work that is designed to be a reference or text book for Grades 4 – 6. It is an odd combination of factual presentation and anecdotal stories about individuals. For example, “When Teddy was two years old, he was diagnosed with a rare form of cancer in his eye. To rid him of the disease, his eye was surgically removed.” Squeamish children may find some content disturbing. To balance the dense text, most pages have a photograph, diagram, or side-bar containing interesting information. There are also “Did you know?” boxes, which allow for some level of interaction. For example, “Did you know? Bionic eyes only provide black-and-white vision. Experts are working on software that would let wearers see colors.” The end of each chapter also has text-based questions and potential research projects. In this way it is more like a text book, but it is unlikely that a classroom would need textbooks on a subject this specific.While the short sentence and paragraph structures are appropriate for the upper elementary reading level, many of the words and word-combinations will be difficult for students in these grades. They may require help in understanding the content. There are few children’s books about artificial eyes, so this would be a good addition to public libraries and both elementary and junior high school libraries. Recommended: 3 stars out of 4Reviewer: Sean C. BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.109 | 0.126 |
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".