Bibliographic record
Abstract
The latest release of Rosen Publishing's Teen Health and Wellness database has retained the richness of award-winning content while adding features, new articles, and Web resources that will further increase its appeal, accessibility, and effectiveness for teens. The addition of video clips, health and personal finance calculators, and the ability for teens to submit personal stories and video segments make this product even stronger for teens of all literacy levels. While Teen Health and Wellness is highly valued and acclaimed in public libraries, the database offers much to educators with inclusion of lesson plans, curriculum correlations, opportunities for students to publish, and widgets that allow librarians to highlight this tool on the library or health teachers’ Web pages. The database interface and content are well targeted to a teen audience, and equally easy to use whether for academic or personal information needs. Teen Health and Wellness is in wide release in the United States, and a version customized to schools, colleges, and public libraries of Ontario, Canada has been in use for the last 3 years. The database will continue to be customized to work well with young people in other countries in the next several months. This product remains the best of its kind as an outstanding resource to serve the difficult problems many teens face, and a powerful tool in helping students develop essential twenty-first century knowledge such as communication, information literacy, personal responsibility, and especially problem solving skills.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.131 | 0.022 |
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".