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Record W7018224496

In/Convenience: Inhabiting the Logistical Surround

2024· article· en· W7018224496 on OpenAlexfundno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
FundersStrategic Research CouncilSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsVolkswagen FoundationEuropean CommissionLeverhulme Trust
KeywordsAffordanceRelation (database)FeelingCapitalismPaymentPower (physics)Everyday lifePoliticsCloud computingEthnography
DOInot available

Abstract

fetched live from OpenAlex

Convenience is the feeling and aspiration that animates our platformed present. As such, it poses urgent techno-political questions about the everyday digital habitus. From next-day delivery, gig work, and tele-health to cashless payment systems, data centers, and policing – convenience is an affordance and an enclosure; our logistical surround. Driving every experience of convenience is the precarious work, proprietary algorithms, or predatory schemes that subtend it. This collaborative book traces how the logistical surround is transformed by thickening digital economies and networked rituals, examining contemporary conveniences across a wide range of practices and geographies. Contributors examine the ineluctable relation between convenience and its constitutive opposite, inconvenience, considering its infrastructural, affective, and compulsory dimensions. Living in convenience is thus both a hyper visible manifestation of so-called late capitalism and a pervasive mood that fades into the background (like the data centers that power it). Bringing the agonistic relation of in/convenience to center stage, this volume analyzes the logistics of delivery, streaming porn, cloud computing, water infrastructures, smartness paradigms, convenience stores, sleep apps, surveillance, AI ethics, and much more – rethinking the cultural politics of convenience for the present conjuncture.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.313
Teacher spread0.272 · 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
Published2024
Admission routes1
Has abstractyes

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