Entangled Affects: \nSite-responsive Experiments Using Actor Training Methods
Bibliographic record
Abstract
This thesis is an ethnography and critical analysis of a practice as research project that I conducted in the Fall of 2017 to investigate three interstitial sites nested around the Concordia University campus in downtown Montreal. I began with the premise that the body, sensitive and responsive to a site, could engage with it through movement and in so doing, render important aspects of the site palpable. I asked: if the body is available to being moved by the site’s affect, what may be understood about the site through that engagement? In order to explore this notion, I conducted a series of experiments which facilitated participants’ site-responsive movements, as generated through exercises drawn from theatre acting training and from a class in somatic approaches to movement. \n \nThis interdisciplinary research is grounded in my theatre practice and informed by theory based in the empirical study of performance methods. By applying the training methods towards engagement with the site, I extended them towards a framing as spatial practices, thus creating a platform for critiquing the site. Through this study, I seek to illuminate connections between the site’s social, historical, and economic contexts, its materiality, and the affects that thread into the participants’ experiences. I aim to underline the importance of affect in interstitial spaces, to demonstrate the potential of embodied performance practices to engage with that affect, and to contribute a methodology for generating and analysing qualitative, embodied, site-responsive data.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".