Bibliographic record
Abstract
Darren Copeland: I think in a lot of cases I’m taking my studio background and trying to apply it to public situations. Playing on the 401 is perhaps an extension of other installations that I’ve done, which have been in public spaces, where I may have been creating something in relation to the visual world, having sound that complements it or comments on it in some fashion. Last November at the Inter active ’05 festival in Toronto I made a piece that was called Intersections. It was on a pedestrian bridge, about 100 feet long, that goes over the train tracks. The bridge connects the two buildings of the Metro Convention Centre. In one of the buildings of the centre, the Toronto Art Fair was going on , and Intersections was a kind of welcoming piece in a sense. I did recordings of the br idge and people going by and stuff-just the sound of people walking through it, and also the sound of the trains underneath as they passed by. I was interested in playing those sounds back into the space and then manipulating them, to kind of alter reality and create a kind of ambiguity between what is real and what is imaginary. And so, sometimes , the sound is quite manipulated and has maybe only a very slight sense of it being a train or someone talking or some one walking, but other times the sound is realistic .There’s that sort of “look over the shoulder” thing that sometimes people have when they think they hear something that’s not actually there. It also kind of altered people’s awareness of space. The bridge was no longer just a way to get from one place to another; it was a place to visit. I think it worked in that way. I’d like to do mor e pieces like that. For the Open Ears festivals in Kitchen er I have been doing pieces in the City Hall rotunda. Those are eight-channel soundscape pieces.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".