New Directions in Site Performance Practice: Intersecting Methodologies in an Era of Climate Coloniality
Bibliographic record
Abstract
In this short piece, I start to tease out the challenges of examining a field of performance that, in theory, deeply understands place but has insufficiently faced the impacts of climate coloniality on place, in part due to the fact that the field has largely been defined and developed by white researchers in the Global North. Specifically, by looking at a recent project called VINES, initiated by me and Brandy Leary, I dig into the site performance field’s investment in workshop-oriented practice-based research to consider what it might mean to intersect posthumanist research methodologies with Indigenous methodologies that emphasize relationality, reciprocity, and accountability.
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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 | 0.194 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.016 | 0.095 |
| Scholarly communication | 0.036 | 0.038 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".