MétaCan
Menu
Back to cohort
Record W1946936000 · doi:10.3390/h4040554

Mapping out Patience: Cartography, Cinema and W.G. Sebald

2015· article· en· W1946936000 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHumanities · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature and Cultural Memory
Canadian institutionsConcordia University
Fundersnot available
KeywordsMovie theaterPatienceNarrativeVariety (cybernetics)Space (punctuation)ArtCartographyArt historyGeographyVisual artsLiteraturePhilosophyComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Cinematic cartography can be an especially powerful tool for deep mapping, as it can convey the narratives, emotions, memories and histories, as well as the locations and geography that are associated with a place. This is evident in the documentary film Patience (After Sebald) by Grant Gee, which follows in the footsteps of W.G. Sebald and his walking tour of Suffolk, England, as described in his book The Rings of Saturn. A variety of strategies in cinematic cartography are used quite consciously in Gee’s exploration of space, place and story. Using Teresa Castro’s three cartographic shapes of cinema, I structure an analysis of the film’s opening scene through a discussion of cinematic cartography, or the plotting of geospatial data onto a map, as well as what I will differentiate as cartographic cinema, or the mapping of space through the cinematographic image. I argue that both are necessary not only to have a deep understanding of the world and our place in it, but also in how to transmit that knowledge to others.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.752
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.110
GPT teacher head0.229
Teacher spread0.119 · 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