Lisbonizing versus Financializing Europe? The Lisbon Agenda and the (un)Making of the European Knowledge-Based Economy
Bibliographic record
Abstract
The Lisbon Agenda was meant to make the European Union ‘the most dynamic and competitive knowledge-based economy (KBE) in the world’ by 2010. As that date has now come and gone, it is apt to ask whether the Lisbon Agenda achieved its objective. We engage with this very question by analyzing new empirical material on the supposed transition to a KBE. Theoretically, we problematize the very notion that EU policies promoted the emergence of a KBE by highlighting how the Lisbon Agenda was tied to the financialization of the European economy. Our findings illustrate the abject failure of the EU's decade-long strategy to foster a new economy and better employment opportunities. We show that the main winners of the EU's economic strategy have been the finance sector and those who work in it. In summary, we argue that, despite the earlier assurances of Bell and Drucker, it is not the scientist or engineer but the banker who has been empowered to command a higher price in the new world of the KBE.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".