Bibliographic record
Abstract
Recent trends in technology are dramatically reshaping academic library collections, and while the use of video in higher education isn't new, the move toward streaming brings a new array of benefits and challenges for academic librarians.LJ recently explored the ways in which libraries are addressing interest in streaming video services.In April 2017, LJ conducted a blind survey of academic librarians in the United States and Canada, sponsored by Swank Motion Pictures, receiving 330 responses.Most respondents-221-are in four-year colleges and university programs, serving an average of 10,392 students,while the remainder are at community colleges or graduate schools.Slightly over half of the schools were in public university systems. STREAMING FROM THE OUTSIDEThe vast majority of responding libraries-95 percent-offered some sort of streaming video content, with a particular focus on documentaries, full-length movies and television programs, and historical archive footage.Of those that offer streaming, 83 percent license their video content from multiple vendors' video streaming platforms, particularly Films on Demand, Kanopy, Alexander Street, and Swank Motion Pictures.Other notable platforms included Ambrose Digital, Swank Digital Campus, Docuseek2, Film Platform, Intelliform, JOVE, and MedCOM.Alexander Street is the vendor from which libraries license the most content, though Films on Demand is the vendor with which they spend the most money.Kanopy was selected as the "most valuable" streaming platform for both students and faculty.Over 90 percent of respondents rely on IP address authentication to access these platforms.About a third use single sign on, while others work with proxy servers, geolocation authentication, or multiple logins. HOSTING AT HOMEOnce you get beyond commercially available content, the numbers drop significantly, though a substantial minority are streaming other content as well: 61 percent of responding libraries provide access to streaming faculty-or student-produced videos.Of those that do, 76 percent host them locally, while 32 percent offer them through a vendor platform.Christine Fischer, head of technical services for the University of North Carolina (UNC), Greensboro, Library, described working with a faculty member who inquired about posting his own film to make it more widely available.The library connected him with vendor Kanopy, and the film is now not only available to UNC students, it is part of the Kanopy catalog.Making that happen was an "interesting and different" library service, she said.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.113 | 0.027 |
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".