At What Point(s) in the Research Lifecycle Can an Art Library Facilitate Visual Art Research?
Bibliographic record
Abstract
This presentation looks at the question “At what point(s) in the research lifecycle can an art library facilitate visual art research?” in relation to an ongoing research-creation project that I began during a spring/summer residency at Artexte in 2018, and that will culminate in a publication to be co-published by this non-profit Canadian arts organization and myself in 2023. \n \nEntitled Who Was Who Was Who in Contemporary Canadian Art, the residency and publication explore and document Canadian artists from the 1960s onwards who use pseudonyms, personae, alter egos and other kinds of alternate identities in their art practice. This bilingual (English/French) publication takes the form of a print and openly accessible artists’ biographical dictionary with distinct but related entries for the artists and their alternate identities. \n \nThrough the use of concrete examples in my presentation, I will argue that an art library like Artexte’s can facilitate visual art research throughout the entire research lifecycle in numerous and invaluable ways.
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 | 0.053 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.048 | 0.039 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.045 |
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".