In/Convenience: Inhabiting the Logistical Surround
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".