Bibliographic record
Abstract
Beyond the fact that all the articles we publish are written concerning studies from various international contexts, we will publish this fifth issue in a bilingual version. It signifies our distinct commitment to stan- dardize the management of this language. Furthermore, we will pre- sent reviews of the most pertinent books to introduce the foremost authors in the global context.This expansion of the scope of our pers- pective will enable us to learn from experiences that will contribute to the consolidation of the profession in Spain.The former linguistic reser- vation no longer holds relevance. Lastly, this linguistic immersion is imperative to avoid exclusion from the proposed lines of work at the 17th World Congress of Music The- rapy scheduled in Vancouver for the upcoming three years, as well as the goals outlined by the World Federation (WFMT). As indicated by Mercadal (2023), its honorary president until this year, these objectives encapsulate music therapy and avalanches, attention to emerging gen- der diversity, and interdisciplinary collaboration.This final point is pivo- tal to our growth and education. We strongly recommend reviewing the conference proceedings book to comprehend the current scienti- fic research directions. These proposals warrant contemplation, study, and pragmatic exploration to effectively address the societal challen- ges of the twenty-first century with the transformative capabilities of music as therapy. Remaining on the sidelines is not a viable option.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.111 | 0.091 |
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".