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Record W153524735

[no title]

2011· article· en· W153524735 on OpenAlexfundno aff
Sónia Matos, Matthew Fuller

Bibliographic record

VenueEdinburgh Research Explorer · 2011
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsnot available
FundersUniversity of California, IrvineSyddansk UniversitetUniversitetet i BergenConcordia UniversityArchitectural League of New YorkMurdoch UniversityQueensland University of TechnologyUniversity of SouthamptonCentral Queensland UniversityUniversity of Technology SydneyUniversity of California, San DiegoUniversity of StirlingUniversity of New South WalesRMIT UniversityLakehead UniversityNational University of SingaporeCurtin University of TechnologyDartmouth CollegeMacquarie UniversityUniversiteit van AmsterdamSwinburne University of TechnologyUniversity of Western SydneyYork UniversityManchester Metropolitan UniversityChapman UniversityJohns Hopkins UniversityStrategiske ForskningsrådUniversità degli Studi di Napoli Federico II
KeywordsUbiquitous computingComputer scienceHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

In ‘The Coming Age of Calm Technology’, Mark Weiser and John Seely Brown are clear in their assertions, what really ‘matters’ about technology is not technology in itself, rather, its capacity to continuously recreate our relationship with the world at large (Brown and Weiser 1996). Even though they promote such an idea under the banner of ‘calm technology’, what is central to their thesis is the mutational capacities brought into the world by the spillage of computation out from its customary boxes. What their work tends to occlude is that in setting the sinking of technology almost imperceptibly, but deeply into the ‘everyday’ as a target for ubiquitous computing, other possibilities are masked, for instance, those of greater hackability or interrogability of such technologies. Our contention is that making ubicomp seamless (MacColl et al, 2002) tends to obfuscate the potential of computation in reworking computational subjects, including societies, modes of life, and inter-relations with the dynamics of thought and the composition of experience and understanding.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.967
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.016
Scholarly communication0.0120.015
Open science0.0010.006
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0330.012

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 head0.347
GPT teacher head0.383
Teacher spread0.035 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes1
Has abstractyes

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