MétaCan
Menu
Back to cohort
Record W3039045498 · doi:10.29173/irie117

Reclaiming the Ambient Commons: Strategies of Depletion Design in the Subjective Economy

2014· article· en· W3039045498 on OpenAlexvenueno aff
Soenke Zehle

Bibliographic record

VenueThe International Review of Information Ethics · 2014
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)CommonsMediationContext (archaeology)Object (grammar)SociologyStructure and agencyImmanenceEpistemologyAestheticsEnvironmental ethicsEngineering ethicsPolitical scienceComputer scienceSocial scienceLawEngineeringArtificial intelligenceArt

Abstract

fetched live from OpenAlex

The vision of an internet of things, increasingly considered in the context of the “internet of everything”, calls for an ethics of technology driven less by the philosophical search for the essence of technology than by a transversal curiosity regarding processes of constitution. If growing interest in enhanced and expanded media literacy approaches facilitates ethical reflection, the scope of such reflection is related to the extent of our attention to and awareness of the immanence of our agency, our capacity for relation in machinic assemblages that structure and sustain our communicative existences far beyond the sphere of signification. While the positions from which such reflection occurs are necessarily multiple, we can still respond to the distribution of agency with an aggregation of responsibility and the creation of a commons with greater attention to the vastness of the spatial and temporal scales of our situation. The idea of depletion design is both a concrete set of design strategies and an attempt to establish an experimental institutional object to facilitate and frame such ethico-aesthetic practice, an architecture for commoning that situates and affirms our ethical agency under the conditions of mediation.

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)0.005
metaresearch head score (Gemma)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.059
GPT teacher head0.310
Teacher spread0.251 · 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

Explore more

Same venueThe International Review of Information EthicsSame topicDigital Media and PhilosophyFrench-language works237,207