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Record W7052808739

The Sounds of Nordic Noir

2024· dissertation· en· W7052808739 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsnot available
FundersUniversity of TorontoOregon State UniversityAarhus UniversitetHarvard UniversityPrinceton University
KeywordsDepictionEthosViewpointsNarrativeSound (geography)Expression (computer science)Window (computing)Sound design
DOInot available

Abstract

fetched live from OpenAlex

Nordic noir, the distinctive form of crime fiction from the Nordic region, has become unprecedentedly successful in this millennium. Its dark crime stories have gained a global audience, especially in audiovisual form, and it can well be said that Nordic noir has become a kind of a window onto the Nordic societies and their mental land-scapes. It mediates images and impressions of the North to the world but also to the Nordic people themselves. Music and other sounds are central constituents of cine-matic expression and, therefore, have a significant role also in audiovisual Nordic noir. However, although Nordic noir has already been studied extensively, its sonic aspects have thus far mainly been left outside analytical scrutiny. This article-based thesis investigates, how music and sound design operate as components of audiovisual expression and narration in representative examples of audiovisual Nordic noir, and more precisely, how music and sound design partake in the socially critical ethos characteristic of Nordic noir, as well as the depiction of the Nordic region and Nordicness. A mixed-method approach is applied, combining qualitative and quantitative research methods with viewpoints especially from cul-tural musicology and phenomenology. The thesis also actively participates in the current critical debate on the nature of Nordic noir. In addition to the summary section, this thesis presents four original research articles. Of the articles, three discuss particular film and television series that can be considered key representatives of audiovisual Nordic noir, focusing in each case on the first season of the series and foregrounding the soundtrack as a central factor in the audiovisual whole. The series are Wallander (Sweden 2005–2013), The Killing (Forbrydelsen, Denmark 2007–2012), and The Bridge (Bron/Broen, Sweden/Den-mark 2011–2018). The fourth research article, in turn, takes a notably larger sample, as it examines the title sequences of Nordic noir series. Thirty-three title sequences in total from nineteen different series were included in the audiovisual analysis. By expanding Nordic noir scholarship to include also the sonic dimensions of the series, the study produces new knowledge on Nordic noir as a cultural phenom-enon. It demonstrates that, in Nordic noir, sounds – music included – are an essential constituent in varied audiovisual strategies, through which the narrative content of the series is linked to the sociocultural reality outside the storyworld. In doing so, the series utilise existing conceptions of the North and the Nordic society and culture and also place them in a critical light. Thus this study also finds that, contrary to what has been stated in some instances – and despite certain observable family resemblances – Nordic noir does not have a specific distinctive sonic character, but the series are notably different in their expression regarding music and sound design – and therefore also their audiovisual appearance as a whole.

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)0.003
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.011
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.305
Teacher spread0.298 · 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
Published2024
Admission routes1
Has abstractyes

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