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Record W7056251554

A escuta do Submundo

2019· dissertation· en· W7056251554 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório da Universidade de Lisboa (University of Lisbon) · 2019
Typedissertation
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSoundscapeActive listeningFocus (optics)Field (mathematics)NarrativeSound (geography)
DOInot available

Abstract

fetched live from OpenAlex

Listening the Underworld: Soundscapes in the artistic context, is divided into two experimental audiovisual installations with special focus on medium sound. Undercurrent and Inland are two immersive installations that demonstrate, each in its own way, through different narrative possibilities of realistic features, subjective and metaphorical sound. Both installations fall within the concept of Soundscape theorized by the Canadian composer R. Murray Schaffer. Shuffling the hierarchy of the senses, responds to cases submitted, outlining several possible paths in their diffusion in the contour image itself and effective demonstration of the potential of this medium in its pure state, when processed and spatialized. To culminate in what is the fundamental focus of the dissertation, unravel the processes contained in the specificity of field recordings when applied to contemporary installation and sound performance, i use several literary references as Whitman, Thoreau, Bachelard, Mumford and Le Breton and others more specific in the art field as R. Murray Schafer, Douglas Khan, Rob Young, Richard Serra or Alvin Lucier. Always in the territory of sound as place in the art I approach to the main protagonists of the twentieth century avant-garde such as Luigi Russolo, Max Neuhaus or John Cage; experimental music in the twenty-first century transition and finally the field recording artists such as Chris Watson, Peter Cusack or Jacob Kierkegaard, who like previous explorers, go wandering and alternating positions between academy, the gallery, the stage and record editions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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