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The March on Washington and a Peek into Racial Utopia

2017· book· en· W4242396105 on OpenAlexaboutno aff
Aniko Bodroghkozy

Bibliographic record

VenueUniversity of Illinois Press eBooks · 2017
Typebook
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsWhite (mutation)UtopiaDreamMedia studiesEvent (particle physics)Political scienceQuarter (Canadian coin)Presidential systemHistoryArtArt historyAdvertisingLawSociologyPoliticsPsychology

Abstract

fetched live from OpenAlex

This chapter examines how television networks handled the coverage of the March on Washington on August 28, 1963 to a national audience of millions. The March on Washington drew a quarter of a million civil rights activists who converged on the nation's capital to press for “jobs and freedom.” Television cameras and reporters focused on the demonstrators' placards and signs. All three networks broadcast the event live. With the exception of presidential inaugurations and nominating conventions, no single event had ever commanded such extensive television coverage. This chapter first considers how the CBS news team framed and packaged the March on Washington as a news story, and particularly Martin Luther King Jr.'s “I Have a Dream” speech, before discussing various responses to the television news reporting of the march in the African American press. It suggests that the March on Washington functioned as a paean of “black and white together,” as the networks invited viewers to share in a utopian taste of achieved equality.

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.061

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 studies0.0070.004
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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