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Record W4387078942 · doi:10.1215/26410478-10437067

Manic History

2023· article· en· W4387078942 on OpenAlexaboutno aff
Christopher Bracken

Bibliographic record

VenueCritical Times · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsMelancholiaIndigenousPsychoanalysisManiaRelation (database)Id, ego and super-egoAgency (philosophy)HistoryPsychologyGender studiesSociologyBipolar disorderPsychiatrySocial scienceLithium (medication)

Abstract

fetched live from OpenAlex

Abstract On May 27, 2021, the Tk'emlúps te Secwépemc First Nation reported the discovery of 215 unmarked graves on the site of the former Kamloops Indian Residential School in British Columbia. Their first response was mourning for the loss of young lives; their second response was melancholia for the loss of the children's names. David Eng and David Kazanjian advocate a “counterintuitive” interpretation of melancholia as “creative,” redefining it as the work of mourning that sustains “a continued and open relation to the past.” Jeff Barnaby's 2013 film about residential school resistance, Rhymes for Young Ghouls, affirms melancholia as a creative relation to the past for Indigenous people while drawing attention to another agency that allows settler society to actively lose the past. Freud remarks that the “most remarkable” quality of melancholia is the way it turns into mania, which ensues when “the ego coincides with the ego ideal.” What if some losses do not make us melancholic but manic? Is it possible to make history by losing history? Settler mania incites Indigenous melancholia by displacing responsibility for children's deaths from church and state to parents who are themselves school survivors.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0870.014

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 head0.040
GPT teacher head0.353
Teacher spread0.313 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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