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Record W2912134496 · doi:10.1386/public.29.58.42_1

Smoke Screens and Cinematic Representations of the MOVE Bombing

2018· article· en· W2912134496 on OpenAlexaff
Nataleah Hunter-Young

Bibliographic record

VenuePublic · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature, Film, and Journalism Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRepresentation (politics)Displacement (psychology)CriminologySociocultural evolutionSociologyState (computer science)HistoryGender studiesVisual artsArtPolitical sciencePsychologyLawAnthropologyPoliticsPsychoanalysis

Abstract

fetched live from OpenAlex

Abstract Premiering in 2013, Jason Osder’s documentary film Let The Fire Burn chronicles the Philadelphia Police Department’s 1985 bombing of the Black revolutionary organization, MOVE. The bombing and subsequent fire – which the police commissioner let burn through 61 homes – resulted in the deaths of six adults and five children as well as the displacement of 250 people. Composed entirely of archival footage, Osder’s representation invokes a historicization and delimitation of an un-ended story about anti-Black state violence that, this essay argues, results in the obscuring of both MOVE’s continued struggle for freedom and the social conditions that make such actions possible. By thinking through collective memories of Black suffering and the ways in which they are mediated amidst a continued global assault on Black people, Hunter-Young explores what the temporal and spatial properties of smoke, and its relationship to fire, may signal about the sociocultural work of Osder’s film.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.041
GPT teacher head0.252
Teacher spread0.210 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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