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Record W193030981 · doi:10.25071/1718-4657.36728

CANADIAN GHOSTS AND THE WILL TO TRUTH: READING MARLENE NORBESE PHILIPS’ LOOKING FOR LIVINGSTONE

2008· article· en· W193030981 on OpenAlexaffvenueabout
Concetta V. Principe

Bibliographic record

VenueIntersections conference journal · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsYork University
Fundersnot available
KeywordsWitnessPhilosophyTRACE (psycholinguistics)Reading (process)Freudian slipLiteratureEpistemologyArtPsychoanalysisPsychology

Abstract

fetched live from OpenAlex

While the ghost may be a device for resolving past issues in literature, its presence in the archive is central to Derrida’s critical approach in Archive Fever. Presenting this paper at an international colloquium “Memory: The Question of the Archives” at Freud’s own archive, Derrida considers Yerushalmi’s dialogue with the ghosted Freud as the desire that drives the archive: “… hauntedness is not only haunted by this or that ghost,… but by the spectre of the truth which has thus been suppressed” (Derrida 1998: 87). For Derrida, truth is a trace as elusive as “ash,” untouchable but always recognizable in its absence, enforcing the Freudian trust in memory as true in part, the search for which, Derrida claims, inspires a sort of illness; thus, the fever of the archive. Derrida recognizes that remembering and repeating are central to the archive as an injunction to bear witness to the past which, according to Derrida, is a responsibility not to those who have passed,but for those who will read in the future.1 Nietzsche’s understanding of the will to truth as that which perpetuates the assumption that truth exists can be understood as implicit to the endlessly repeated aporia of Derrida’s ghost (Nietzsche 1956: 288). This spectre is made visible by the will to truth of a witness for specific future time and place.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.523
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.021
Scholarly communication0.0080.007
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.034
GPT teacher head0.218
Teacher spread0.184 · 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
Published2008
Admission routes3
Has abstractyes

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