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Record W2087221627 · doi:10.1353/scp.2012.0012

Sustainability and the Scholarly Enterprise: A History of Gutenberg-e

2012· article· en· W2087221627 on OpenAlexvenueno aff
John T. Seaman, Margaret B. W. Graham

Bibliographic record

VenueJournal of Scholarly Publishing · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipPublishingSustainabilityScholarly communicationPolitical scienceSociologyHistoryLibrary scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

This article analyses the origins, development, and impact of Gutenberg-e, a digital publishing program in historical scholarship sponsored by the American Historical Association (AHA), with the support of the Andrew W. Mellon Foundation. Intended as an experiment in developing and legitimizing new modes of historical scholarship, Gutenberg-e quickly evolved, under pressure to become economically sustainable, into a traditional publishing enterprise bent on making books cheaper and paying for itself in the process. Digital technology, which had the power to transform the whole scholarly enterprise, instead became a means to shore up the existing system of scholarly publishing, with all its flaws intact. Though Gutenberg-e has much to teach us about the costs and consequences of that system, especially for the scholars it is meant to serve, it also offers a glimpse of an alternative future. Almost in spite of itself, Gutenberg-e produced a handful of innovative works of digital scholarship, experimented with new forms of scholarly collaboration and community, and highlighted the opportunities of an expanded audience for specialized academic work. These modest achievements suggest the potential of digital technology to create things which scholars value and thereby sustain the scholarly enterprise over the long term.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.009
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0300.209
Open science0.0010.000
Research integrity0.0000.002
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 head0.041
GPT teacher head0.226
Teacher spread0.185 · 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

Labeled directly by 2 models reading the full record.

Scholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Qualitative
Domainnot available
GenreEmpirical

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

Citations3
Published2012
Admission routes1
Has abstractyes

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