A Piece of String and a Little Imagination: An Interview with Chris Wheeler
Bibliographic record
Abstract
When I started working at the Stratford Festival in 2003, one of the blessings of the organization that was pointed out to me early on was that we had our own MacGyver. Chris Wheeler (a.k.a. “Wheeler” or “Wheels”) held and still holds the singular position of Electronics Technologist. In this capacity, he is the go-to man when the director, the set designer or lighting designer wants something magical to happen onstage that requires some kind of electrical wizardry. He is also an in-house software programmer. Our lighting inventory is filled with “Wheeler” strobe lights, “Wheeler Brains” for remote dimmers and effects and scores of random lighting toys, all with a “Wheels” or “Wheeler” prefix, unique creations that he’s been building for decades. As an assistant lighting designer, one of my primary responsibilities was to keep Wheeler in the loop for what tricks each show needs: how many dimmable lanterns in each show? how long are they on for? how many lighting effects are built into the set? does the radio light up? how many channels of DMX control is the monkey puppet going to need? and when is everything needed by? Most of that work happens in his shop in the Festival Theatre complex, where I sat down with Chris on a snow-covered January day in Stratford to have a chat.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.025 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".