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Record W4360778865 · doi:10.1007/978-3-031-14109-6_14

Speaking Up: How Early Career Workers Engage in Fighting for Better Working Conditions by Joining Youth-Led Social Movement Organisations

2023· book-chapter· en· W4360778865 on OpenAlexaff
Maite Aurrekoetxea-Casaus, Edurne Bartolomé Peral, Günter Hefler, Ivana Studená, Janine Wulz

Bibliographic record

VenuePalgrave studies in adult education and lifelong learning · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPosition (finance)Industrial relationsPublic relationsMovement (music)Social movementPolitical scienceSocial activismBusiness

Abstract

fetched live from OpenAlex

Abstract Learning from activism, usually informal and unrecognised, is an important component of industrial relations and a major learning source for individuals, organisations and society. Young workers who lack support from existing employee organisations may create their own. Based on studies of social movement organisations in highly diverse industrial relations systems (Austria, Spain’s Basque Region, Slovakia), this chapter presents a framework for analysing and comparing novel social movement organisations’ position within industrial relations systems. Each was founded because its national system did not adequately address challenges. Activism enables young people employed in workplaces unfavourable to learning, or unemployed, to compensate for what better workplaces offer. Youth-led social movement organisations generate important knowledge and practical skills, challenging established organisations, including trade unions, and renewing industrial relations structures.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.067
GPT teacher head0.335
Teacher spread0.268 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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