Bibliographic record
Abstract
As a participant in and witness to a turning point in the history of photography – the transition from analog to digital – I examine the iconic character of the darkroom through its postindustrial ruins. Exploring the notion of mourning (the foundation of my work), I offer a kind of damage inspection report, like a claims adjuster searching for clues at the ‘scene of the crime.’ On a desecrating, sacrilegious quest, I defy the death of the panchromatic spectrum and the meteoric rise of computing, and harshly illuminate the stopping up of parasitic light, the mechanics of the enlargers, the electric odds and ends, the maze of plumbing, the air ducts, the silver-salt stains, and the countdown of the timers. Calling the history of photography to witness, my research endeavors to contribute to the emblematic role of the darkroom as a unique creative environment, without an equivalent in the universe of image reproduction technologies. Digital photographs in this portfolio were made in Berlin, Montreal, Brussels and Paris. The overall project also included photographs of darkrooms made in Havana, Toronto, Niamey, Ho Chi Minh City and Mexico FD. This photographic project was generously supported by the Canada Council for the Arts and the Conseil des Arts et des Lettres du Québec, most recently through the Jean-Paul Riopelle Career Grant.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".