Bibliographic record
Abstract
Slegers, Liesbet. Dentists and What They Do. Clavis Publishing Inc., 2017. Intended to be read by parents with their toddlers, Dentists and What They Do is a fun, highly informative guide to a first visit to the dentist's office. Brightly-coloured images and diagrams occupy portions of every page of the story. These minimalist drawings are annotated with rhythmic phrases such as “shine, shine” for a mirror or “how funny!” for a dentist’s mask, making the book entertaining to read. Whereas the text of the story itself is small, the images are labelled in a larger font, and are therefore intended to be read by young children, introducing a variety of vocabulary. Further, Slegers accommodates for any fears that young children may have before their first visit to the dentist by demonstrating ways in which medical professionals act to entertain and accommodate children. For example, the dentist checks the teeth of a stuffed animal. Overall, Dentist’s and What They Do is delightful, easy to read, and likely to be enjoyed even by older children because of its engaging format. In its demystification of the first trips to the dentist, the book is an excellent way to introduce normal checkups and appointments to young children and is effective in making the process entertaining. Highly Recommended: 4 stars out of 4Reviewer: Madeline C. Crichton Madeline Crichton is a University of Alberta undergraduate student with a lifelong passion for reading. When she is not preoccupied with her studies, Madeline is busy volunteering in a variety of roles in her community.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.103 | 0.087 |
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".