Selecting Fiction Books for a Children's Health Collection by M. Tan & S. Campbell
Bibliographic record
Abstract
Books have long been recognized resources for health literacy and healing (Fosson & Husband, 1984). Individuals with health conditions or disabilities or who are dealing with illness, disability or death among friends or loved ones, can find solace and affirmation in fictional works that depict characters coping with similar health conditions. This study asked the question “If we were to select a new collection of children’s health-related fiction in mid-2014, which books would we select and what selection criteria would we apply?” The results of this study are a set of criteria for the selection of current English language literary works with health-related content for the pre-kindergarten to Grade 6 (age 12) audience http://hdl.handle.net/10402/era.38842, a collection of books that are readily available to Canadian libraries - selected against these criteria http://hdl.handle.net/10402/era.38843, a special issue of the Deakin Review of Children’s Literature - dedicated to juvenile health fiction, and book exhibits in two libraries to accompany the Deakin Review issue.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".