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Record W7097099207

The appropriation of a digital “Speakers’ Corner”: Lessons learned from the deployment of Mégaphone

2014· article· en· W7097099207 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware deploymentAppropriationField (mathematics)DowntownEthnographySet (abstract data type)Order (exchange)Space (punctuation)Public space
DOInot available

Abstract

fetched live from OpenAlex

Interactive digital technologies embedded in urban spaces typically tend to be used to deliver news, context-relevant information and commercial advertisements. To design urban technologies that will serve other ends, we first need to know how people might want to interact with them. Using an ethnographic approach, we collected field data in order to better understand this. This study presents some of the findings of our qualitative evaluation of MÉGAPHONE, an interactive artistic installation deployed in a public space in downtown Montréal, Canada. In this paper, we provide thick descriptions of our detailed field observations and interviews with participants conducted over the ten-week deployment with a deep focus on how users appropriated this system. Our results highlight four public interaction strategies as a set of abstractions that suggest how people might want to make use of interactive public installations: place-making, self-representing, first-person news reporting and bootstrapping online presence with digital recordings. Author Keywords Interactive public art installations; urban technology; voice-

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.689
Threshold uncertainty score0.175

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 science0.0010.000
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 head0.051
GPT teacher head0.283
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2014
Admission routes1
Has abstractyes

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