Diversity in Libraries: The Case for the Visible Minority Librarians of Canada (ViMLoC) Network
Bibliographic record
Abstract
In a January 2012 post on the technology blog The Verge, Nilay Patel describes a shift in the way content is consumed that has resulted in the re-emergence and reinvigoration of Digital Rights Management (DRM), defined by the Electronic Frontier Foundation as a system that attempts "to control what you can and can't do with the media and hardware you've purchased." 1 Patel's article, "DRM Comes Back with a Vengeance as Digital Media Moves to the Cloud," is telling because it reflects a trend that is not commonly acknowledged by either consumers or people working in libraries.Patel paints a picture of a drastically different landscape than that of the late 1990s.In the 90s, it seemed as if DRM were declining, punctuated by the fact that, in 1997, music sold through Apple's iTunes became completely DRM-free.As Patel writes, "the success of DRM-free music sales would seemingly prove that a thriving digital economy does not require technological limitations on consumer behavior." 2 But this was not to be, partially due to the fact that there has been a significant shift in the way people consume digital content.The days when users purchase individual digital files and download them the way they buy physical items in a store are quickly disappearing.Digital formats like e-books, music, and video are now frequently stored in the "cloud" and purchased on a monthly basis.In addition, services like Netflix, Rdio, and Spotify are replacing the traditional video and audio libraries that we once owned.According to Patel, DRM has also changed, becoming more flexible, easy to use, and invisible to consumers.Cloud-based distribution models are nothing new for libraries.The difference in the library world is that many of our cloud-based services, like our research databases, are not protected by DRM, making us living proof that these services can exist without widespread
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".