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Record W4312754406 · doi:10.2307/j.ctv33t5ggk.25

Primary Rights and the Inequalities of E-Book Access

2020· book-chapter· en· W4312754406 on OpenAlexaff
Roën F. Janyk, Arielle Lomness

Bibliographic record

VenuePurdue University Press eBooks · 2020
Typebook-chapter
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaOkanagan CollegePurdue Pharma (Canada)
Fundersnot available
KeywordsInequalityPolitical scienceComputer scienceInternet privacyMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.984
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.192
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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