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Record W2539378628 · doi:10.5703/1288284316283

Tough Love: Guiding Student Researchers Toward a Better Future for E‐Books

2016· article· en· W2539378628 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsNews aggregatorScarcityComputer scienceMobile deviceWorld Wide WebInternet privacy

Abstract

fetched live from OpenAlex

EPUB has emerged as the standard format for e‐books due to its numerous advantages over PDF, including superior accessibility, enhanced navigation, lighter file sizes, optimization for mobile devices, and support for non‐English languages, to name a few. However, there is little understanding of EPUB’s advantages among end users and little appreciation for EPUB’s potential in academic libraries. This paper provides a literature review and perspectives from a publisher, an aggregator, and end users (higher education library) about solutions that drive increased knowledge and use of the EPUB format for e‐books in the academic library. It will summarize the reasons for EPUB’s ascendance among academic publishers, explain how PDF e‐books present barriers to innovation and real problems for accessibility, and argue that the scarcity of EPUB in the academic library is mostly due to a lack of awareness of its benefits. It will give librarians some ideas on how to begin integrating EPUB into research instruction, and ultimately, it will suggest that both librarians and vendors take an active role in shaping the habits and thus demands of their users.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.169
GPT teacher head0.425
Teacher spread0.256 · 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

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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