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Record W4381737013 · doi:10.1177/02685809231180880

‘What have you done to our world?’: The rise of a global generational voice

2023· article· en· W4381737013 on OpenAlexafffundabout
Cécile Van de Velde

Bibliographic record

VenueInternational Sociology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversité de Montréal
FundersCanada Research Chairs
KeywordsInjusticeDemocracyRhetoricSocial movementSociologyPoliticsPolitical economyMovement (music)Gender studiesSocial injusticeFalse accusationMedia studiesPolitical scienceAestheticsLaw

Abstract

fetched live from OpenAlex

Based on a comparative analysis of seven youth movements, this article shows the rise of a rhetoric of intergenerational injustice over the past decade, increasingly associated with a direct accusation of older generations and with a generational and global ‘we’. Theoritically, we propose to approach the ‘generational voice’ – rather than the generational ‘presence’ – to shed light on the generational grievances, emotions and identities carried within movements. We draw on the textual analysis of protest slogans (n = 1914) collected directly from: the Indignados (2011), the student movements in Chile and Quebec (2011–2012), the Paris ‘Nuit Debout’ movement (2016), the Hong Kong pro-democracy movements (2014 and 2019), and the Montreal pro-climate march (2019). Using mixed methods, the article shows the existence of four major rhetorics of generational injustice – be it economic, social, political or environmental – associated with an increasingly radical critique of a legacy, deemed too heavy for ‘future generations’.

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)0.006
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.017
Scholarly communication0.0060.004
Open science0.0000.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.411
Teacher spread0.363 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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