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Record W4321436298 · doi:10.2307/jj.362382.8

Controversing Datafication through Media Architectures

2023· book-chapter· en· W4321436298 on OpenAlexfundno aff
Corelia Baibarac Duignan, Julieta Matos Castaño, Anouk Jacoba Petronella Geenen, Michiel de Lange

Bibliographic record

VenueAmsterdam University Press eBooks · 2023
Typebook-chapter
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekCanadian Institute of Steel Construction
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.256
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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