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Record W4302774259 · doi:10.1080/1369801x.2021.2003228

Introduction: Infrastructure as an Inter-Asian Method

2022· article· en· W4302774259 on OpenAlexaff
Xiao Liu, Shuang Shen

Bibliographic record

VenueInterventions · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsMcGill University
Fundersnot available
KeywordsSuperpowerForegroundingThe ImaginaryHegemonyPolitical scienceCritical infrastructurePower (physics)Competition (biology)SociologyState (computer science)Regional scienceChinaPoliticsComputer scienceLaw

Abstract

fetched live from OpenAlex

The idea for this special issue grew out of a workshop the co-editors organized at the Conference on “InterAsian Connections VI: Hanoi” in December 2018. This special issue conjoins critical infrastructure studies with inter-Asian perspectives in an effort to transcend a techno-nationalist framework of infrastructure as solely an instrument of state power and superpower competition. We are particularly interested in exploring the “deep time” that critical infrastructure studies bring to the examinations of local and transregional connections in Asia and beyond. We argue that the “deep time” of infrastructure is evident in the ways in which previous and existing social relations are mobilized, appropriated, transformed, obscured or occluded with newer layers of infrastructural development. Furthermore, the essays collected here demonstrate self-reflection on the pertinence of the study of infrastructure to the production of knowledge about regions and regionalization in general, foregrounding such questions as “What is gained by adopting an infrastructural approach on ‘Asia’ as a social and cultural imaginary?” and “What does it mean to call something infrastructural?”

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)0.007
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 score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0310.004

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 head0.018
GPT teacher head0.360
Teacher spread0.342 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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