Bibliographic record
Abstract
Manon Labrecque is a multidisciplinary artist based in Montreal, Canada. Her work is an encounter between the banal and the grandiose; the human body and the machine; the useless and the essential. A true polymath, Labrecque eludes categories and labels. A fan of handmade work, her relationship with technology unfolds through a lo-tech, autonomous aesthetic that engages the whole body. In her work – whether experimental video or multimedia installation – life and death continually collide in an eloquent fashion, evoking in turn the value of human life and the programmed obsolescence of the technologies that populate our world. From an aesthetic of the ordinary, she brings out layers of meaning underlying the tidiness of everyday life, notably through various techniques of disrupting and glitching the image. A mischievous artist, Labrecque is interested in an alternative beauty, that of failures and technical bugs. Her techniques of hacking and editing brings to the fore the fragility of video materials. In this way, the artist takes on the role of an image witch: her skilful technical hijackings reveal material-bodies and machine-bodies that bear the marks of fallible technology, while at the same time exuding an undeniable yet strange, even reinvented humanity.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| 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".