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Record W1919293927 · doi:10.22230/src.2017v8n2a281

Bedfellows in Mass Digital Conversion: Ten Years of Text Creation Partnership(s)

2017· article· en· W1919293927 on OpenAlexvenueno aff
Aaron McCollough

Bibliographic record

VenueScholarly and Research Communication · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipMandatePublic relationsPolitical scienceCommonsBusinessSociologyLaw

Abstract

fetched live from OpenAlex

This paper offers a brief account of some senses in which “partnership” and collaboration have been and continue to be fundamental to the Text Creation Partnership’s mandate. Additionally, by examining the kinds of collaborative engagements in which TCP has participated, it raises and addresses questions about some frictions produced by unlikely partnerships between the private and public sectors as well as about benefits afforded by the same. Finally, it tenders some suggestions about future collaborative efforts the TCP might help to foster and, in turn, be fostered by. Ultimately, this paper stresses cooperative protection of the digital cultural commons as a cardinal virtue of digital humanities collaborative effort. It seeks to refocus attention on this aspect of the TCP’s role in the field.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0160.035
Scholarly communication0.0260.026
Open science0.0020.027
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.148
GPT teacher head0.355
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations0
Published2017
Admission routes1
Has abstractyes

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