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Record W4206947301 · doi:10.5040/9781350232310

Sonic Histories of Occupation

2022· book· en· W4206947301 on OpenAlexfundno aff
Jeremy Taylor

Bibliographic record

VenueBloomsbury Publishing Plc eBooks · 2022
Typebook
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastUniversity of Nottingham
KeywordsColonialismSound (geography)PoliticsLicenseHistoryMedia studiesSociologyPolitical scienceLawArchaeologyAcoustics

Abstract

fetched live from OpenAlex

This open access book examines how auditory environments in different contexts have contributed to understanding foreign occupation and colonialism, and how they have given rise to historical music cultures. How are sound and music implicated in the control and discipline of people under occupation? Exploring case studies of foreign occupation and colonialism from around the world, Sonic Histories of Occupation seeks to answer these questions and more. Examining how an emphasis on auditory culture adds complexity and nuance to understanding the relationship between occupation and the bodily senses, this book is structured around three conceptual themes: voice and occupation; memory, sound and occupation; and auditory responses to occupation and colonialism. Highlighting case studies in Asia, North Africa, North America and Europe, contributors employ a range of theoretical approaches to examine histories of imperialism and foreign occupation, and the auditory legacies they created, and contribute to a wider dialogue about the relationship between sound and imperial projects across political and temporal boundaries. The open access edition of this book is available under a CC-BY-NC-ND 4.0 license on www.bloomsburycollections.com. Open access was funded by the European Research Council (Horizon 2020, Grant Number 682081).

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 score0.016
Threshold uncertainty score0.055

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 studies0.0050.009
Scholarly communication0.0060.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.094
GPT teacher head0.218
Teacher spread0.124 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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