Bibliographic record
Abstract
The auditory sound structure of the city has changed radically over the past century. Industrial and technological developments have not only increased the quantity of the urban sound elements but have also enhanced their volume. Simultaneously, since the introduction of the Sony Walkman in the 1970s, the phenomenon of mobile privatization through the widespread use of headphones can be observed. This practice may be seen as a way of escaping the complexity of the contemporary urban soundscape by focusing instead on the experience of different content through headphones. Artistic projects, so-called Mobile Sound Art, also use this tool, but seek to evoke a strong engagement with the surrounding environment. This thesis explores this phenomenon and questions how the emotional relationship to urban space is impacted by Mobile Sound Art smartphone applications. The descriptions are situated in a critical analysis informed by a range of urban and critical theorists, such as the Canadian composer R Murray Schafer, who has dealt intensively with contemporary urban soundscapes. Through the combination of two methods, namely the Walk- Through-Method to explore the functioning of the smartphone applications studied and the bodily exploration of urban space through the practice of Soundwalking, I experienced the different urban environments differently. Both methods are based on an introspective practice and on subjective experiences of the smartphone applications and the urban space. It is evident that these experiences are strongly influenced by situational factors such as mood and weather. At the same time, the selected smartphone applications, Soundtrackcity and VUSAA, significantly impact the emotional relationship to the urban space explored. Although this connection is characterized by a historical component, i.e. whether the individual already is familiar with the environment and connects experiences, the applications are able to shape these spaces anew.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".