Tanya Lukin Linklater : Video Symposium : Writing and Performing = Vidéo Colloque : écrire et performer
Bibliographic record
Abstract
"Tanya Lukin Linklater often makes performances with dancers and sometimes composers/musicians in relation to objects in exhibitions, scores, cultural belongings and the structure of the museum, reaching towards atmospheres that shift the space or, potentially, the viewer. Her work centres knowledge production in and through orality, conversation, and embodied practices, including dance. While reckoning with histories that affect Indigenous peoples’ lives, lands and ideas, she investigates insistence. Her work is currently on view at …and other such stories, the Chicago Architecture Biennial 2019 and at SFMOMA’s Soft Power organized by curator of contemporary art, Eungie Joo. Her forthcoming book of poetry, Slow Scrape, will be published in the Documents series by The Centre for Expanded Poetics and Anteism, Montréal. Tanya studied at University of Alberta (M. Ed.) and Stanford University (A.B. Honours) and is a doctoral candidate in Cultural Studies at Queen’s University." -- Publisher's website.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.032 |
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".