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Record W1542506993 · doi:10.29173/irie182

Dejetos Materiais e Informacionais como Elementos Culturais

2009· article· en· W1542506993 on OpenAlexvenueno aff
Raquel Rennó

Bibliographic record

VenueThe International Review of Information Ethics · 2009
Typearticle
Languageen
FieldComputer Science
TopicInformation Science and Libraries
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

To reflect on waste is to think of it beyond cycles of consumption, as an integral element of cultural processes. The essay broadens the analysis of waste to speak of cultural remainders in a more comprehensive sense, including populations that exist (and have to subsist) at the margins of the official city, often through acts of reappropriation generally considered piracy. Such reappropriation is central to the informal economies whose control evades the cybernetic approaches to governance, and whose cultural logic offers a counterrationality to dominant processes of consumption. Piracy is a strong element of informal economies, and its mode of production and distribution operates through fragmentation, occurring in the interstices of the city or in an ephemeral manner in order to escape surveillance. Strategies of evasion and the fluidity of the market of illegal goods, the ephemeral appropriation of space by street peddlers, by garbage collectors and inhabitants of residual spaces offers a broader view of a dynamic of info-technological recodification that is not restricted to groups of tactical media, political activists, or the terrain of digital media such as Internet and mobile telephony.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0060.029
Scholarly communication0.0170.015
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.033
GPT teacher head0.326
Teacher spread0.293 · 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.

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
Published2009
Admission routes1
Has abstractyes

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