Bibliographic record
Abstract
Goodnight Noises Everywhere consists of several performative soundwalks around Vancouver's Commercial Drive neighbourhood, exploring the question: how can vulnerability, experienced through the intersection of public space and private life, be used as a creative resource?Vulnerability is examined through b o d y response to uncertain environments and circumstances: sites in the midst of change and unexpected events unfolding in public space.Tracing influence from interventionists, post-modem dance and relational aesthetics, the project connects to a heage committed to contesting the line between art and life.By creating an unexpected dlssolve between art event and life event, the soundwalks invoke a sense of vulnerability in both audience and performers that in turn creates immediacy and intimacy of experience that is unique, temporal, and, hopefully, gives rise to further creativity.This is vulnerability as creative resource, the art in the project.I am grateful to be living in a relatively safe space in the world to be doing this thing called art.I am grateful that I was able to have spent so much time making art with Aretha and Lori.I am grateful to Jacky for being a nurturing bridge between art maker (collaborator) and outside eye (sounding board) before, during and after the process.I am grateful to have
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.014 |
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".