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Record W2592105381 · doi:10.1386/des.5.1-2.12_1

A great and downward hunt: How to design inorganic trauma

2016· article· en· W2592105381 on OpenAlexaff
Ben Woodard

Bibliographic record

VenueDesign Ecologies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsExpansiveDramatizationRelation (database)HistorySociologyLiteratureArt

Abstract

fetched live from OpenAlex

Abstract While many of the physical sciences have historically and conceptually informed philosophy, geology is one field that has not received adequate philosophical attention. In what follows, I attempt to demonstrate how the relation between the geological and the philosophical is over-coded in numerous historical and fictional contexts in the form of geotrauma, the violence of the earth or the inorganic on the organic, and the violence of the inorganic on itself. Historico-mythical tales of the violence of mining and surveying often centre on the geological landscape as offering a form of violence, or as invading the consciousnesses of those around it with a form of ancient violence. In this regard, the earth’s geological forces can be treated as murderous agents, where investigating the crime simply seems to redouble the violence. The article examines how this strange and violent reflectivity deploys itself in an exaggerated way in weird and gothic fiction. Looking at the works of Fritz Leiber, H. P. Lovecraft and Charles Brockden Brown, this article attempts to argue how the fictionalization of geotrauma is a dramatization of the already existent co-implication of geology and philosophy in the form of a co-murdering and the traumatic nature of the expansive concept.

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)0.010
metaresearch head score (Gemma)0.023
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: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0070.034
Scholarly communication0.0130.011
Open science0.0030.009
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0220.003

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 head0.048
GPT teacher head0.277
Teacher spread0.229 · 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
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
Published2016
Admission routes1
Has abstractyes

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