Painting as mending structure: Landon Mackenzie in dialogue with Jacqueline Davidson
Bibliographic record
Abstract
Illness and memoir come together in the handwritings of people struggling to overcome illnesses such as cancer, where the writer collides with established narratives such as “winning the battle”. Faced with an illness with no tidy or clear diagnosis from 2001 to 2005, Canadian artist Landon Mackenzie turned to painting as the logical language to help unravel and depict her hunches, nervous system research and experiences in a series of works called Houbart’s Hope (2001–2005), as well as other new works on canvas or paper. The studio is her place to think, not the keyboard. Using her skill set as an experienced artist, her condition forced her to work in new ways, while she used the “text” of images, colour and form. In her large-scale canvases, which are over two by three meters each, complexity itself was foregrounded. She very slowly was able to create a group of major new works. Using her cartography research, and in particular the historic search for the Northwest Passage from 1611 to the twentieth century as a parallel to her own understanding of the unknown, “brain as a new frontier”, she engaged her artistic methods to understand an illness with no pre-established narratives or images as she recovered. She made a memoire of illness none the less. Mackenzie refers to painting as a mending structure.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.015 |
| 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".