Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.117 | 0.030 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".