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Record W1717183130 · doi:10.22230/src.2012v3n4a70

Fluid Layering: Reimagining digital literary archives through dynamic, user-generated content

2013· article· en· W1717183130 on OpenAlexaffvenue
Jon Saklofske

Bibliographic record

VenueScholarly and Research Communication · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsAcadia University
Fundersnot available
KeywordsDigital ArchivesDigital humanitiesWorld Wide WebComputer scienceDigital contentFunction (biology)Field (mathematics)Foundation (evidence)CommonsHistoryPolitical scienceArchaeologyLaw

Abstract

fetched live from OpenAlex

This article promotes a theoretical evolution in the conceptualisation and operation of digital literary archives via NewRadial, a prototype archive application that models the following distinction: Whereas a digital edition continues to function as a primary source, the root of a secondary discourse field much like its print-based predecessor, the digital archive should be reconceived as a broader, active, dynamic public record, an information commons that substantiates a foundational collection of primary texts with a continuous aggregation of critical contexts and conversations that grow from that foundation.

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)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0050.012
Scholarly communication0.0200.029
Open science0.0030.017
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.176
GPT teacher head0.319
Teacher spread0.143 · 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 designNot applicable
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
Published2013
Admission routes2
Has abstractyes

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