A Survey of Music Faculty in the United States Reveals Mixed Perspectives on YouTube and Library Resources
Bibliographic record
Abstract
A Review of: Dougan, K. (2016). Music, YouTube, and academic libraries. Notes, 72(3), 491-508. https://doi.org/10.1353/not.2016.0009 Abstract Objective – To evaluate how music faculty members perceive and use video sharing sites like YouTube in teaching and research. Design – Survey Questionnaire. Setting – 197 music departments, colleges, schools, and conservatories in the United States. Subjects – 9,744 music faculty members. Methods – Schools were primarily selected based on National Association of Schools of Music (NASM) membership and the employment of a music librarian with a Music Library Association (MLA) membership. Out of faculty members contacted, 2,156 (22.5%) responded to the email survey. Participants were asked their rank and subspecialties. Closed-ended questions, ranked on scales of 1 to 5, evaluated perceptions of video sharing website use in classroom instruction and as assigned listening; permissibility as a cited source; quality, copyright, and metadata; use when items are commercially unavailable; use over library collections; comparative ease of use; and convenience. An open-ended question asked for additional thoughts or concerns on video sharing sites and music scholarship. The author partnered with the University of Illinois’ Applied Technology for Learning in the Arts and Sciences (ATLAS) survey office on the construction, distribution, and analysis of the survey data through SPSS. The open-ended question was coded for themes. Main Results – Key findings from closed-ended questions indicated faculty: used YouTube in the classroom (2.30 mean) more often than as assigned listening (2.08 mean); sometimes allowed YouTube as a cited source (2.35 mean); were concerned with the quality of YouTube recordings (3.58 mean) and accuracy of metadata (3.29 mean); and were more likely to use YouTube than library resources (2.62 mean), finding it easier to use (2.38 mean) and more convenient (1.83 mean). The author conducted further analysis of results for the nine most reported subdisciplines. Ethnomusicology and jazz faculty indicated a greater likelihood of using YouTube, while musicology and theory/composition faculty were more likely to use library resources than others. There was little significant difference among faculty responses based on performance subspecialities (e.g. voice, strings, etc.). Overall, open-ended faculty comments on streaming video sites were negative (19.3%), positive (19.3%), or a mixture of both (34.1%). Themes included: less use in faculty scholarship; a need to teach students how to effectively use YouTube for both finding and creating content; the value of YouTube as an audio vs. video source; concerns about quality, copyright, data, and reliability; and benefits like easy access and large amounts of content. Conclusion – Some faculty expressed concern that students did not use more library music resources or know how to locate quality resources. The study suggested librarians and faculty could collaborate on solutions to educate students. Librarians might offer instructional content on effective searching and evaluation of YouTube. Open-ended responses showed further exploration is needed to determine faculty expectations of library “discovery and delivery” (p. 505) and role as the purchaser of recordings. Conversations between librarians and faculty members may help clarify expectations and uncover ways to improve library resources and services to better meet evolving needs. Finally, the author recommended additional exploration is needed to evaluate YouTube’s impact on library collection development.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".