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Record W2887836700 · doi:10.7202/1048829ar

Hermeneutica, une expérience numérique de l’interprétation : Hermeneutica. Computer-assisted interpretation in the humanities, de Geoffrey Rockwell et Stéfan Sinclair

2017· article· fr· W2887836700 on OpenAlexvenueno aff
Ariane Mayer

Bibliographic record

VenueSens public · 2017
Typearticle
Languagefr
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyPhysics

Abstract

fetched live from OpenAlex

Avec Hermeneutica. Computer-assisted interpretation in the humanities (MIT Press, 2016), Geoffrey Rockwell et Stéfan Sinclair s’interrogent sur les transformations de l’interprétation de textes dans le milieu numérique. En particulier, au travers d’une méthodologie hybride faisant dialoguer réflexions et exemples, théorie et pratique de l’interprétation, ils réfléchissent à ce que les outils d’analyse textuelle assistée par ordinateur révèlent et infléchissent dans l’activité herméneutique. Au cœur de l’essai se trouve l’outil Voyant, espace numérique de quantification et de visualisation textuelle développé par les coauteurs, qui leur sert de matériau pour aborder les mutations contemporaines de la lecture dans les sciences humaines.

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)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.016
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0050.039
Scholarly communication0.0160.025
Open science0.0020.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.037
GPT teacher head0.317
Teacher spread0.280 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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