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Record W2785780634

Mean time: seven ways to look at time and mobility

2016· article· en· W2785780634 on OpenAlexaboutno aff
Cidália Ferreira Silva

Bibliographic record

VenueRepositóriUM (Universidade do Minho) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsnot available
FundersUniversidade do Minho
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper’s idea was triggered by the exhibition “Cedric Price: Mean Time,” presented at the Canadian Centre for Architecture in Montréal. Starting with the premise that mobility is a contingent (Till, 2009) act, this text looks at the different time(s) created by this contingency. The seven time(s) here considered are: Suspending Time, Free Time, Expanding Time, Distorting time, Folded Time, Loosing time, and Living time. Through specific “spatial stories” (de Certeau, 2002) each time is explained, in their features, unfolding how time-mobility shapes the way we create different appropriations of space, transmuting not only places, but also the relationship between ourselves and the other. The faith in progress gives us the sensation of a non-stopping improvement of mobility, namely the infrastructures, systems and technology. It seems that everyday we have new ways to displace ourselves in space that are faster, and better. More speedy trains, more airplane flights, more cars, more...Nonetheless, we know that this linear time of progress is coexistent with other parallel time lines of disruption and failure. For example, the Portuguese trainroad known as “Linha do Oeste” has a long story of planned suspension attached to it: not only suffering from underinvestment and lack of maintenance but what is more with timetables that do not fit the needed working rhythms of users. All these are at the basis of what the detractors reclaim as the argument for suspension: the lack of users. By suspending time, human beings are being conditioned in their living mobility, with consequences that are far beyond this physical infrastructure and its occupied space. This is only a particular time case explored in this mean time.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.011
GPT teacher head0.251
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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