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Record W2024158071 · doi:10.1080/08854300902904949

Re-Building Infrastructures of Resistance

2009· article· en· W2024158071 on OpenAlexaff
Jeffrey Shantz

Bibliographic record

VenueSocialism and Democracy · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsDissentResistance (ecology)GlobalizationFace (sociological concept)Political economySocial movementPolitical scienceSocialismWorking classSociologyPoliticsLawSocial scienceCommunism

Abstract

fetched live from OpenAlex

It is sometimes said that while anti-capitalist and alternative globalization movements are clear on what we do not want, we are less clear on what we do want (socialism, anarchism, specifics). Certainly, recent movements have not been as effective as their predecessors (labor in the 1910s and 1930s; the social movements of the 1960s and 1970s) in sustaining the sorts of practices ‐ intellectual and material ‐ that put into effect aspects of the alternative world we seek. My colleague Alan Sears attributes this current inability to a decline in what he calls “infrastructures of dissent” or what I prefer to call “infrastructures of resistance.” As anti-capitalist movements face possibilities of growth, as happened after Seattle in 1999, questions of organization and the relation of various activities to each other and to broader movements for social change can only become more urgent. Yet, the absence of durable organizations or institutions, formal or informal, rooted in working-class organizations and communities, makes for demoralization or a retreat into subculturalism, as has happened to many of the alternative globalization groups. We now face a pressing need to rebuild “infrastructures of resistance” that might sustain not only activists and organizers, but especially the poor and working-class people who are being disastrously impacted by the current crisis.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.312
Teacher spread0.300 · 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

Citations12
Published2009
Admission routes1
Has abstractyes

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