Environmental Sound and Urban Noise: Ben Rubin and Jer Thorp’s <i>Herald/Harbinger</i>
Bibliographic record
Abstract
“Environmental Sound and Urban Noise” looks at the impact of sound in the experience of Ben Rubin and Jer Thorp’s Herald/Harbinger. This work by two New York-based artists is installed in the centre of the Calgary city downtown to invoke, in Thorp’s words, “a long-distance conversation between a glacier and a city.” Real-time data is collected from geophones embedded into the Bow Glacier, some 220 km west of Calgary. The data is then translated into aural form (by way of an algorithm devised by Rubin and Thorp) and relayed via satellite, with a mere 5-minute delay, to a sixteen-channel speaker installation located on the forecourt of the city’s tallest building, Brookfield Place. The installation at once provides a place of repose and an injunction to listen. The glacier’s soundtrack heralds the past and the present, the Pleistocene and the Anthropocene eras, Indigenous and settler populations, natural and built environments. At the same time, the installation is a harbinger, asking its audiences to listen to where our climate crisis seems destined to go. The “conversation” hailed by Herald/Harbinger is all the more poignant and most certainly urgent in a city whose economic prosperity remains overdetermined by the fossil fuel industry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".