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Record W4200428209 · doi:10.5195/errs.2021.557

Space of Experience, Horizon of Expectation. Spatiotemporal Metaphors, Philosophical Anthropology, and the Flesh

2021· article· en· W4200428209 on OpenAlexfundno aff
Roger Savage

Bibliographic record

VenueÉtudes Ricoeuriennes / Ricoeur Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
FundersUniversity of TorontoUniversity of OxfordFordham UniversityUniversity of PittsburghNorthwestern University
KeywordsPhenomenology (philosophy)DialecticHistoriographyNarrativeReciprocalEpistemologySpace (punctuation)AestheticsOntologyFleshRelation (database)PhilosophyHistorySociologyLiteratureArtArchaeologyLinguistics

Abstract

fetched live from OpenAlex

Paul Ricœur’s recourse to the metahistorical categories, space of experience and horizon of expectation, invites an inquiry into geography’s role as the guarantor of history. The ontology of the flesh provides the first indication of how one’s body is implicated in the sense of one’s place in the world. In turn, narrative inscriptions of events on the landscape transform the physical topography of a place into an array of sites where memories of ancestral wisdom and historical traumas endure. By anchoring historians’ representations of the past in the places and locales in which events took place, geography constructs a third space analogous to the third time of history. The aporias engendered by the phenomenology of time, however, have no equivalent in the phenomenology of space. The dissymmetry between the dialectic that informs the discourse of space and the one that informs the discourse of time thus keeps in place the reciprocal relation between geography and historiography.

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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.062
Scholarly communication0.0080.010
Open science0.0010.004
Research integrity0.0030.003
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 head0.053
GPT teacher head0.304
Teacher spread0.251 · 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
Published2021
Admission routes1
Has abstractyes

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