MétaCan
Menu
Back to cohort
Record W4327905840 · doi:10.9744/kata.25.00.9-14

On “Multitude” and the Urban Question: Reading in Times of Pandemics

2023· article· en· W4327905840 on OpenAlexaff
Abidin Kusno

Bibliographic record

VenueK@ta · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsYork University
Fundersnot available
KeywordsMultitudeContext (archaeology)IndonesianPoliticsSociologyReading (process)PandemicPolitical sciencePolitical economyEconomic growthPublic relationsCoronavirus disease 2019 (COVID-19)EconomicsLawGeography

Abstract

fetched live from OpenAlex

“Multitude” is a term popularized by Antonio Negri and Michael Hardt to conceptualize the labor condition and its political possibilities in the post-Fordist regime of capital accumulation. This paper seeks to explore such a concept in the context of an Indonesian city. It argues that the Indonesian multitude is formed through the worldwide division of labor, which involves the urban majorities whose work cut across formal and informal sectors. It teases out the absence of the “urban question” in the Indonesian city as a context for understanding the challenges faced by the Indonesian multitude. The paper (in light of post-pandemic) calls for the role of the state to serve as a medium for achieving societal goals and a guarantor of public access to Universal Basic Assets covering education, health, housing, technology, and information.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0060.022
Scholarly communication0.0100.017
Open science0.0010.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.275
Teacher spread0.234 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueK@taSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207