Controversing Datafication through Media Architectures
Bibliographic record
Abstract
In this chapter, we discuss a speculative and participatory “media architecture” installation that engages people with the potential impacts of data through speculative future images of the datafied city. The installation was originally conceived as a physical combination of digital media technologies and architectural form—a “media architecture”—that was to be situated in a particular urban setting. Due to the COVID-19 pandemic, however, it was produced and tested for an online workshop. It is centered on “design frictions” ( Forlano and Mathew, 2014 ) and processes of controversing ( Baibarac-Duignan and de Lange, 2021 ). Instead of smoothing out tensions through “neutral” data visualizations, controversing centers on opening avenues for meaningful participation around frictions and controversies that arise from the datafication of urban life. The installation represents an instance of how processes of controversing may unfold through digital interfaces. Here, we explore its performative potential to “interface” abstract dimensions of datafication, “translate” them into collective issues of concern, and spark imagination around (un)desirable datafied urban futures.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".