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Record W4396707158 · doi:10.11647/obp.0371.17

17. Language and Power

2024· book-chapter· en· W4396707158 on OpenAlexaff
Luke Clossey

Bibliographic record

VenueOpen Book Publishers · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The deep ken saw power and meaning in the word “Jesus” itself, as that word consonated with Jesus himself. Where the plain ken understood the meaning of a word as accidental, the deep ken perceived its meaning hardwired into the fabric of the universe. The best way to fathom the deep-ken power of “Jesus” is to watch the cult develop around the name itself, most successfully promoted by Bernardino of Siena. More broadly, the name “Jesus” was applied to a variety of people, places, and things: A number of “Jesuses” governed the Ottoman empire; the largest “Jesus” had two masts and shot iron darts. Sometimes Jesus language not only harmonized with the universe, but was understood to have the power to manipulate it. Studying the ways cultists used “Jesus,” often in conjunction with relics and mathematics, to maximize their spiritual and material well-being illustrates the deep ken's logic of language.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.001

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 head0.024
GPT teacher head0.313
Teacher spread0.289 · 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 teacher head, not a consensus.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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