Notes from the Field: A Weekend in Toronto’s Old-Time Music Community
Bibliographic record
Abstract
I leave Buffalo, N.Y., around 7:00 pm on Friday night, after working a full shift at my retail job. The night is cold, but the sky and roads are clear, and crossing into Ontario on the Peace Bridge is quiet, as I seem to be the sole traveler headed north that night. Around Hamilton, the weather turns and the last 40 km are a brutally slow crawl into Toronto. I’m learning to think in kilometers. I’m learning to watch the weather. I arrive at the Russian Orthodox Church near The Annex neighborhood of Toronto for the square dance around 10:00 pm, two hours late. Regardless, I feel welcomed and expected. People are taking a break. We talk about the weather and the turnout for the dance – fewer than hoped for, though still sizeable, and plenty for this first test run of the square dance they plan to continue. They have enough for three full squares, which means there are at least 24 people there, plus the band and caller. While it may seem small, I think it is significant, given the fact that it is a frigid Friday night in late January and it has been snowing since the early afternoon. Hannah is calling, all dressed up in her red gingham dress. The band calls itself Whistlehog. Sarah plays fiddle, Sean is on banjo, Heather is on guitar, and Lucas is on bass. They have played once or twice together before, but Sean is about to leave to work on a farm further north. This will likely be their last time playing together.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.122 | 0.026 |
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".