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Record W4390653090 · doi:10.5840/symposium202327218

Russon's Method of Authorless Description

2023· article· en· W4390653090 on OpenAlexvenueno aff
Gregory Kirk

Bibliographic record

VenueSymposium · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Theory and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsHegelianismOpenness to experienceRealmTRACE (psycholinguistics)EpistemologyDemocracyPhilosophySociologyPolitical scienceLawPsychologySocial psychology

Abstract

fetched live from OpenAlex

In this article, I present John Russon’s phenomenological method of authorless description. I trace this method to Russon’s engagement with Aristotle, Hegel, and Heidegger. Speci􀏔ically, I claim that he is informed by Aristotle’s practice of accounting for appearances, Hegel’s method of presuppositionless science, and Heidegger’s project of preparation to “let being be.” I apply this to Russon’s book, Sites of Exposure, and his account of both the human need to transcend the home towards an open-ended realm of indifference and the concrete development of the conditions in which that is made possible in what we call the modern world. I present his account of the emergence of representative democracy, modern science, and glob-al capitalism. I argue that Russon’s method provides essential tools for understanding the promises and failures of what we call the modern world and the imperative of openness that ought to guide us in striving to address those failures.

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)0.022
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0070.038
Scholarly communication0.0090.019
Open science0.0040.008
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0140.005

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 head0.061
GPT teacher head0.381
Teacher spread0.320 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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