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Student Campaigns

2020· book-chapter· en· W4213234243 on OpenAlexaboutno aff
Donatella della Porta, Lorenzo Cini, César Guzmán‐Concha

Bibliographic record

VenuePolicy Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsMarketizationPolitical sciencePoliticsDemocracyLeft-wing politicsSocial movementUnitary statePolitical economyPublic administrationSociologyLaw

Abstract

fetched live from OpenAlex

This chapter illustrates the temporal trajectories and the main characteristics of the recent student mobilizations, occurring in the four cases under investigation, that oppose measures promoted by national governments to foster a neoliberal model of higher education. In exploring the goals, strategies, and action repertoires of such mobilizations, it notes similarities and differences between the actors involved in the protests within and across the four regions. To begin with, students have various traditions of activism in the four cases studied, which have informed contemporary movements. Moreover, in the four cases, the mobilization campaigns have shown a surprisingly high (especially for England and Quebec) confrontational orientation, exemplified by the adoption of very disruptive protest tactics, such as street blockades, and railway and university occupations. Similar also were the main demands and goals pursued by the students, who were concerned with the negative consequences of the process of marketization affecting their universities and their lives, and the support of the restoration of a stronger public system with a more democratic outlook. Yet, some key differences across the four cases were identified in the various capacities of students to build unitary protest fronts and to make alliances with other social and political actors, such as leftist political parties and trade unions — a capacity which was higher in the Quebec and Chilean cases, and lower in the Italian and English ones.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.951
Threshold uncertainty score0.931

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)0.0010.000

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 head0.160
GPT teacher head0.413
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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