Bibliographic record
Abstract
The arts have always had a profound impact on our lives: whether it be to express an idea, emotion, or as a way to communicate. Our worldly artistic experiences and knowledge date as far back as the Pre-historic period. So why is it that music, dance, drama, and visual arts, all cultural subjects so steeped in human history are, for the most part, always on the cutting block in educational curriculum? Why is artistic importance in the curriculum always viewed as mundane and devalued by so many policy makers? Following her best selling book Why Our Schools Need the Arts, author Jessica Hoffman Davis brings us Why Our High Schools Need the Arts. In arguing for an increase in arts courses within the educational curriculum as a way to engage all students and to alleviate the drop out rates of disenfranchised youth, Hoffman Davis offers vivid accounts from students, teachers, administrators “to provide the reader (the high school student, concerned parent, school administrator, teacher, arts education advocate, and/or policymaker) with the necessary information and perspective with which to argue for a prominent place for the arts in the reformation of high school curriculum” (p. 5).
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".