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Forging the Past

2016· book· en· W4243897257 on OpenAlexaboutno aff
Daniel P. Marrone

Bibliographic record

VenueUniversity Press of Mississippi eBooks · 2016
Typebook
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeAmbivalenceAestheticsDisappointmentPhenomenonComicsArtHistoryLiteraturePsychologyPsychoanalysisEpistemologyPhilosophySocial psychology

Abstract

fetched live from OpenAlex

The work of Canadian cartoonist Seth positions itself between history and memory, and in doing so gives rise to a range of ambivalent impulses, chief among them an ambivalent longing for the past. Seth suggests that “the whole process of cartooning is dealing with memory,” and by consistently drawing attention to seams and borders, his comics invite the reader to examine the processes by which narratives of the past come to seem seamless. Seth’s work exhibits a complicated nostalgia that is well aware of its own reactionary, restorative and nationalistic inclinations, and is able to channel them toward productive ends. His ironic, humorous, and metafictional approaches to memory, loss and longing for the past reveal that his attitude toward these closely related subjects is deeply ambivalent. Memory is here conceived not just as an invisible, ubiquitous mental phenomenon that reflects our experience of time and relation to the past, but as a medium, an art –one which is in many ways akin to cartooning. The fundamental operation of comics as a visual medium initiates and makes space for narrative interpolations in a way that is not only comparable to but in a certain sense mimics the historical interpolations of memory; in both cases, longing is spurred by incompleteness. Seth turns the medium of memory on itself, using it as an instrument to examine the processes of remembrance and making history.

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: Other · Consensus signal: Other
Teacher disagreement score0.079
Threshold uncertainty score0.157

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 studies0.0200.027
Scholarly communication0.0100.009
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.023
GPT teacher head0.178
Teacher spread0.155 · 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
GenreOther

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

Citations4
Published2016
Admission routes1
Has abstractyes

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