Appel à contributions : Numéro spécial sur "L’éducation en acoustique"
Bibliographic record
Abstract
Acoustics is a vast and multidisciplinary field, employing hundreds of professionals across Canada in areas such as teaching, research, consulting, and more. To highlight the diverse work within this community, Canadian Acoustics has been publishing special journal issues for over 40 years, focusing on various thematic topics related to acoustics. In this spirit, Canadian Acoustics invites submissions for an upcoming special issue, scheduled for March 2025, with a focus on education in acoustics. Manuscripts submitted for this issue may explore (but are not limited to) the following topics: Innovative teaching methods in acoustics Curriculum design and development in acoustic education Integration of acoustics in STEM programs Case studies on acoustic education in different sectors (higher education, technical schools, industry) Use of technology and digital tools in teaching acoustics Educational outreach and public engagement in acoustics HOW TO PARTICIPATE? To contribute to this special issue on "education in acoustics," authors are invited to submit their manuscripts through the online system at http://jcaa.caa-aca.ca, under the “Special Issue” section, by December 1st, 2024. All submissions will be reviewed by the Canadian Acoustics Editorial Board, following the journal's publication policies (e.g., original content, non-commercialism). For further details, please refer to the Journal Policies section online. WHY YOU SHOULD SUBMIT This special issue will serve as a comprehensive resource on education in acoustics across Canada. It will be distributed in hard copies to all national and international members of the Canadian Acoustical Association (CAA), with electronic versions available in open-access on the journal website. Articles will be indexed and searchable through major search engines, including Google and Bing. Authors are encouraged to choose their keywords carefully to maximize the visibility of their work. For any questions, please contact Mr. Olivier Robin at olivier.robin@usherbrooke.ca. This is a unique opportunity that only comes around every 7 to 9 years—don’t miss out!
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.298 | 0.155 |
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".