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Record W2795846424 · doi:10.22329/p.v12i2.5034

Onto-Technics in Bryant, Harman, and Nancy

2018· article· en· W2795846424 on OpenAlexvenueno aff
Susanna Lindberg

Bibliographic record

VenuePhaenEx · 2018
Typearticle
Languageen
FieldPsychology
TopicPhenomenology and Existential Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsOntologyMaterialismEpistemologyPhilosophyNaturalismExplicationRealism

Abstract

fetched live from OpenAlex

My hypothesis in this article is that it is possible to use the philosophical concept of technics to solve a conflict in contemporary continental ontology between speculative materialist and (post)phenomenological approaches. More precisely, I will show that technics gives a privileged access to ontology because it leads to a “materialist” ontology, avoiding both theological and nihilistic approaches, and because technics, being by definition a domain of artificiality, precludes any explication of it in terms of naturalist materialism. I start by critically examining two techno-ontologies that come from speculative realism and object-oriented ontology: Levi R. Bryant’s onto-cartography and Graham Harman’s tool-being. I then present, as a counter-position, Jean-Luc Nancy’s idea of “ecotechnology”. In conclusion, I evaluate the usefulness of the concept of technics for ontology and argue that Nancy’s post-phenomenological approach is preferable because it avoids indefensible hypostases and is more attentive to its own discursive status.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.017
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.027
Scholarly communication0.0050.008
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.350
Teacher spread0.315 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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