Austerity and its Aftermath: Neoliberalism and Labour in Argentina
Bibliographic record
Abstract
The crisis Argentina faced in the late 1980s legitimized a diagnosis that linked the country’s poor economic performance to an inward-looking economy, excessive fiscal spending, unwarranted state regulations, a misguided set of incentives that failed to boost competitiveness and the “economic populism” that privileged political goals over economic efficiency. Alternatively, the solution was sought in policies that privileged deregulation, the free flow of commodities and capital, privatization and a selective intervention of the state in the economy. In this article we will account for the shape of neoliberal restructuring in Argentina by drawing attention to the heavy costs stabilization imposed on the country as the decade progressed. We will emphasize the costs the workers were called on to bear and the responses that emerged from them to challenge neoliberalism. La crise qui a frappé l’Argentine à la fin des années 1980 a justifié un diagnostic qui liait la faible performance économique à plusieurs facteurs : le caractère endogène de son économie, les dépenses excessives de l’État, les réglementations mal avisées, les stimulants mal ciblés qui ne sont pas parvenus à soutenir la compétitivité et le « populisme économique » qui privilégiait les finalités politiques plutôt que l’efficacité économique. En réponse à ce diagnostic, les solutions privilégiées visaient la déréglementation, la libre circulation des marchandises et du capital, les privatisations et l’intervention ciblée de l’État dans l’économie. Cet article présente la configuration des réformes néolibérales en Argentine en insistant sur les coûts élevés que la stabilisation a entraînés au cours de la décennie. Nous soulignons l’importance du fardeau imposé aux travailleurs et travailleuses ainsi que leurs réactions pour contrer le néolibéralisme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".