A year (in five months) of living dangerously: hidden intimacies in Zoom exigencies
Bibliographic record
Abstract
This article invites a meditation on the hidden intimacies Zoom engenders, even as we are constantly reminded that this is not a platform intended to create intimacy. Rather, it is one intended to create work-place efficacy, balancing the ‘happiness’ of distributed or outsourced employees with what Jonathan Crary (2013 Crary, Jonathan. 2013. 24/7: Late Capitalism and the Ends of Sleep. New York: Verso. [Google Scholar]) identifies as a 24/7 availability. Though Jon McKenzie cites the three key characteristics of performance to be efficiency, efficacy, and effectiveness in his foundational book Perform Or Else: From Discipline to Performance (2000), efficacy might be the quality most contradictory to the very ephemeral nature of performance itself. Through a close reading of Troy Anthony and Jerome Ellis’s made-for-Zoom performance Passing Notes (2020) as well as a different performance by Ellis, a consideration of how remote teaching informs not just the form of our pedagogy but its content, and an engagement with the personal vulnerabilities and anxieties Zoom meetings and classes and performances present, I argue that Zoom simultaneously creates divergent intimacies and reframed subjective experiences within the theatre and classroom as living spaces, even as, in its very mechanical, technological nature, Zoom makes ever more clear the distance we are all currently experiencing from each other.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".