Bibliographic record
Abstract
The e-book landscape is in a constant state of flux.More recent developments include new acquisition models, advances in platform usability and navigation, more lenient DRM provisions, and improvements to simultaneous user access licenses.However, what has not been addressed recently are the inequalities in e-book access for libraries across the world due to primary rights.Territorial rights versus world rights is a licensing issue affecting libraries globally, and yet little is being done to address the inequalities of access.Join our discussion that will examine the "unavailable in your country" message libraries often see alongside e-book purchase options, review documented inflation and deflation in e-book prices over time, and learn about the delayed or limited e-book offerings for global libraries.Explore how we can ensure equal access to electronic books for libraries across the globe.Hear perspectives from libraries inside and outside of the United States, as well as publisher thoughts on the topic, including the continued drawbacks for library e-book access they believe will continue.Where do these discussions need to occur and who can we educate on the importance of including international access clauses in licenses or publishing agreements?Although this issue may not be widely known by librarians in the United States, the exclusivity of electronic content based on the geographical location or status of a country is a sharp contrast to many of the inherent beliefs that are foundational to our profession.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 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".