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Shake, Rattle, and Roles: Lessons from Experimental Earthquake Engineering for Incorporating Remote Users in Large-Scale E-Science Experiments

2007· article· en· W2128243924 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer-Mediated Communication · 2007
Typearticle
Languageen
FieldComputer Science
TopicMobile Crowdsensing and Crowdsourcing
Canadian institutionsUniversity of Toronto
FundersU.S. Department of Defense
KeywordsCyberinfrastructureExploitCitizen scienceScale (ratio)ShakeComputer scienceCrowdsourcingPoint (geometry)Data scienceComputer securityEngineeringRemote sensingWorld Wide WebGeography

Abstract

fetched live from OpenAlex

While there has been substantial interest in using e-science and cyberinfrastructure technologies to enable synchronous remote participation in experimental research, the details of such participation are in question. On the one hand, there is a desire to give remote participants the same views and capabilities that they would have as local participants. On the other hand, there are settings where experimental specimens and apparatus are large and difficult to manipulate effectively or view from a remote vantage point. This article argues for more novel forms of remote participation by drawing on exploratory interview and observation data gathered in civil engineering laboratories. It is shown that, while experiments are in progress, the engineers studied focus primarily on detecting and preventing specimen failures, and that their unease about remote participation stems from doubts about the ability of remote participants to detect failures adequately. It is argued that this presents the opportunity to consider novel roles for remote participants that exploit the features of e-science technologies.

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.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.282
Teacher spread0.265 · 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