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The Audiovisual Style of Malcolm Clarke’s Oral History Documentary

2023· article· en· W4389397132 on OpenAlexaff
Dian Xu

Bibliographic record

VenueCommunications in Humanities Research · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsRealmNarrativeFilmmakingStyle (visual arts)Oral historyVisual artsAestheticsShot (pellet)HistoryLiteratureSociologyArtMovie theaterAnthropology

Abstract

fetched live from OpenAlex

This article is primarily dedicated to an in-depth examination of the audio-visual style employed by documentary filmmaker Malcolm Clarke. This analysis is achieved through a meticulous dissection of scenes, shot composition, and sound elements within three of his notable oral history documentaries, namely, A Long-Cherished Dream, The Lady in Number 6, and Soldiers in Hiding. The article initiates by offering insights into the concept and evolution of oral history documentaries as a distinct subgenre within the broader documentary realm. Subsequently, the article embarks on a critical evaluation of how Clarkes documentaries align with the specific attributes and expectations associated with oral history documentaries. It scrutinizes how Clarke adeptly harnesses a diverse array of cinematic techniques to unearth and present the often-overlooked facets of history, which remain concealed or marginalized within the grander narrative of historical discourse. Through this comprehensive analysis, the article provides a comprehensive assessment of Malcolm Clarkes contribution to the realm of documentary filmmaking and his unique approach to elucidating the lesser-explored dimensions of 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)0.001
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.008
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.454
GPT teacher head0.439
Teacher spread0.015 · 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
Published2023
Admission routes1
Has abstractyes

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