Soundwalking as a Means of Building Intergenerational Bridges and Community
Bibliographic record
Abstract
What might the soundscape of a rural historic bridge tell us about ourselves? This thesis examines how soundwalking created opportunities for intergenerational connection, by inviting participants living in a common geographical area to explore a particular bridge, its soundscape and oral history. The historic Avoca Bridge, located in the rural community of Avoca in Quebec’s Lower Laurentians, is the materialization of commonality between members of different generations as they cross over the Rouge River. During the fall of 2023, I led a research-creation intervention with five older adults and one teenage participant, during which they were introduced to amplified and deep listening, and learned about the oral history of the Avoca Bridge. The culminating activity was a soundwalk on the bridge, where participants used digital voice recorders and headphones to amplify the site-specific soundscape. This paper is divided into three chapters. Chapter 1 explores delay as affective response, and as a sound effect. Chapter 2 examines material aspects of the bridge and how these are parallel to participants’ responses. Chapter 3 offers a brief history of soundwalking, with particular attention to Andra McCartney’s seminal work on the practice, followed by a discussion of theoretical and methodological considerations relating to the use of amplification. The overall experience is described and examined through excerpts from participant-created audio recordings, recorded group conversations, and my own notes. The notion of a society where beneficial intergenerational connections are made possible thanks to an interruption to age-segregated practices is explored.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".