Practice for Itself: An Interview with Jason Gladue (Restaurant BALNEA)
Bibliographic record
Abstract
For a while, I mostly knew Jay Gladue, who is a friend and the partner of a very good friend, from attending epic Thanksgiving dinners at their house; and from the music he would play after dinner. For the crowd of drunk friends, eager to sing along, his repertoire consists of covers of old-timey country songs, interspersed with a mix of classics (The Rolling Stones’ “Dead Flowers” is a personal favorite) and idiosyncratic choices (it’s through Jay’s rendition of “The Blarney Stone” that I first discovered the band Ween whom, incidentally, he views as an early influence on his culinary career1). Then, last December, he invited me to photograph a recording session for his current musical project, The Taxidermy. For the occasion, the four members of The Taxidermy had rented the mythical Grand Lodge No. 24, a church-turnedrecording- studio in Farnham, Quebec, that was, until recently, owned by the band Arcade Fire. […]
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".