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Record W4403924825

The Role of Music in Work Activities

2015· article· en· W4403924825 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldMedicine
TopicTechnology and Human Factors in Education and Health
Canadian institutionsAlberta Centre for Advanced MNT Products (Canada)
Fundersnot available
KeywordsWork (physics)Visual artsComputer scienceArtEngineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

This article discusses the role of music in work activities by considering music an intersemiotic discursive practice. It uses an interdisciplinary approach composed of discourse analysis, ethonomusicology and ergology in order to be able to examine the topic including music and work factors, put together by discourse. Firstly, the boundaries of traditional work songs are expanded through two unconventional examples: one in an advertisement, and the other one in a product packaging. Then through that expanded framework, it explores examples of two Brazilian communities, one from Bahia and the other one from Maranhão (both in Brazilian Northeast) that sing while they work in extractivist and agricultural activities. The conclusion points out that the relationship between music and work far surpasses the traditional framing of work songs. To better understand the role of music in the discourse of work activities, scholars need to take into account the renormalization which is brought into play and the discursive communities concerned by those complex discursive practices.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0070.026
Scholarly communication0.0090.006
Open science0.0010.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.326
GPT teacher head0.576
Teacher spread0.250 · 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
Published2015
Admission routes1
Has abstractyes

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