Black Box Exposures: Enriching Public Engagement with Human-Data Relations Through Intermedial Performance Strategies
Bibliographic record
Abstract
Through a combination of this artistic and scholarly research rooted in praxis, this dissertation demonstrates how the medium of theatre provides an accessible, alternative laboratory space wherein the changing relations between humans, data and informatics may be examined. This study begins by examining several topics: the diverse array of analogies and metaphors that currently characterize data out in the world; popular notions surrounding data as a material for ‘telling stories’ about events, objects or people; and growing communities of practice that prioritize intimate and hand-made forms of engagement with data as material for creative expression. In the process, the metaphor of a black box is established as an evocative means of conjoinment between theatre studies, critical data studies and information design, while identifying three salient cross-disciplinary themes—velocities, assemblages, and representativeness. These three themes provide a useful framework for examining several recent theatrical productions wherein aspects of data production, processing and representation are juxtaposed with lived human experiences. These themes are further elaborated through two praxis-based experiments designed and run by the author: a collection of intermedial performance prototypes and an experimental university course. Drawing upon methods and materials developed by the Quantified Self Movement and the Data Humanism Movement, both experiments emphasized strategies of creative resistence in which the activities of data production, processing and representation are explored through forms of enactment. The resulting contribution is a provisional model for both artistic and pedagogical praxis in which these activities are translated into ‘stages of informatic enactment.’ Infused with the methods, practices and sensibilities of the Quantified Self and Data Humanism movements, this model encourages intimate and bespoke forms of creative engagement with data in a performing arts context, but with a greater emphasis on exploring the underlying systems and processes that produce it.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".