From Interview to Interrogation: The Rhetorical Frames of Talk in the Art of Emmanuelle Léonard (Montréal)
Bibliographic record
Abstract
When I was in kindergarten my class participated in making a time-capsule video, as we were the blessed children of the mercurial-sounding “Class of 2000.” Daryl Hadley’s dad worked the camera and Mrs. Bow sat us around the circular orange table in groups of four and conducted the interviews. The questions were standard: what is our favourite toy, what do we like to do on the weekend, what do we want to be when we grow up, etc. It was going fine until Mrs. Bow turned to me and asked, “What do you think is the most important difference between children and adults”? My reply: “When you’re little you can fit into little spaces and when you’re bigger you can go in bigger spaces.” I knew it was a terrible answer as soon as I heard the words come out of my mouth, but too late—it had been committed to both memory and VHS tape. I have never quite forgiven myself for this failure at both ingenuity and authenticity.
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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.039 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".