Bibliographic record
Abstract
Expressed in various art forms and across diverse media, narratives communicate universal themes that enable audiences to identify with the work despite references to culturally, geographically and temporally-specific details. As non-visual carriers of narrative, stories and music in particular rely on the involvement of the audience’s imagination to visualize scenes, atmospheres and places. These interests orient research into the complex interrelationships between the arts of narrative, music and architecture to develop a translative framework for architectural and urban design that yields atmospherically rich spaces inspired by place-specific narratives. Focusing on Sudbury, Ontario, four sites were selected in the downtown core for their connections to overlooked local (hi)stories apropos of the railway, the surrounding rock blackened from mining exploits, and buried creeks. Significantly, these sites are also neglected, or at best underused areas in the city, which the proposed design interventions tackle in order to stimulate an urban revitalization that shifts the emphasis away from a carcentric urban fabric to a more pedestrian friendly, and environmentally and culturally sustainable experience. Culminating in the design of a Mist Park, a Connective Garden, an Urban Terrace and a Resource Centre with an exterior plaza, this thesis project celebrates local identity in its translation of narrative and ambient soundscapes into locally “attuned” public spaces, offeringa critical-poetic commentary on how a design approach interwoven with (hi)story and sound can contribute to a network of sensitive architectural responses that instigate an inclusive, ground-up urban revitalization.
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 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 | 0.024 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".