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Record W2886566262 · doi:10.1080/23269995.2018.1461341

Augmenting the Left

2018· article· en· W2886566262 on OpenAlexafffundabout
A.T. Kingsmith, Julian von Bargen, Karen Murray, Robert Latham

Bibliographic record

VenueGlobal Discourse · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsYork University
FundersYork University
KeywordsConceptualizationMarxist philosophySociologyCapitalismPoliticsNew LeftContext (archaeology)Relation (database)Political economyPolitical scienceGender studiesSocial scienceMedia studiesLawHistory

Abstract

fetched live from OpenAlex

Nine papers with respective replies, grounded in Marxist traditions, analyse the potential for social transformation through a reinvigorated radical Left, all within the context of the ascendance of the far Right worldwide. Papers variously take up new lines of analysis, while also identifying and theorizing strategies and possibilities for increasing and deepening popular participation and support on the far Left. Authors are drawn variously from Australia, Britain, Canada, Cyprus, France, Greece, Ireland, Japan, Slovenia, and the United States. They comprise new scholars as well as established and leading theorists and activists. Collectively, the papers address three predominant themes: the changing and expanding conceptualization on the Left of contemporary capitalism; what it means to speak of ‘the people’ in relation to political action today; and approaches to mobilizing and organizing that people. The wide-ranging but focused and rigorous insights produced across the pieces are aimed at speaking to scholars, students, observers, and activists who seek knowledge about the challenges and opportunities the Left faces today.

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0080.024
Scholarly communication0.0160.024
Open science0.0020.017
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0210.003

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 head0.023
GPT teacher head0.400
Teacher spread0.377 · 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 designNot applicable
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
Published2018
Admission routes3
Has abstractyes

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