Streaming Media Licensing and Purchasing Practices at Academic Libraries: Survey Results
Bibliographic record
Abstract
University instructors and students have a huge appetite for streaming media, and demand is growing apace as the format achieves mainstream market dominance over earlier technologies such as DVDs. University libraries purchase and license the bulk of this content intended for campus-wide (as opposed to personal) consumption and are facing numerous challenges associated with integrating streaming media into their collecting strategies. Pricing models can make it difficult to fully meet patron demands, and some collections feature content that is not consistent with the library’s mission. To compound matters further, streaming media businesses focused on providing educational content are rapidly evolving due to mergers and acquisitions and the development of new business models among the sector’s dominant companies. As the urgency of these issues has grown, researchers have undertaken several important efforts to track how libraries are approaching the streaming media market and troubleshoot the challenges they are encountering, focusing especially on strategies for balancing patron demand with managing costs. Building on those data gathering efforts, this report shares findings from the most comprehensive survey to date of academic library streaming media approaches at four-year institutions in the US and Canada.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".