The Long and Winding Road: Touring Canada with Two Planks and a Passion Theatre
Bibliographic record
Abstract
“Where the Hell am I?” I awake in state of panic. The sheets cling to me in a clammy tangle, the heater next to my bed roars and my partner doesn’t respond to my call for assistance. Where the hell is she? I try to find a light switch, a lamp, but I don’t know what direction to reach in. My hands run up and down walls without any discernible features. No switch. A bedside table? I find it with my left knee, knocking over a bottle onto the carpet below, the cold water running in a torrent around my bare feet. No lamp. Some distance away, a tinny clock-radio erupts midway into “Don’t Worry, Be Happy,” oblivious to my predicament. I follow the sound across the bed to another table, this one with a lamp — but where’s the switch? On the bottom, on the top, where the hell is the damn switch? Finally, success. A blinding light reveals a room that resembles a crime scene photo, minus my silhouette in chalk. The smell of obsolete Chinese takeout and dirty laundry permeates the room with an evil potpourri, and I move to the curtains and throw them open to reveal … a brick wall. Where am I? I dial “0” to get some information and hang up before a voice answers. What am I going to do? Ask, “Where am I, please? I’ve lost track?” That would guarantee an uncomfortable visit from guest services.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.126 | 0.015 |
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".