The Territorial Dimension of the European Social Fund
Bibliographic record
Abstract
The European Union Treaty of Lisbon brought a new dimension to cohesion – the territorial dimension, which has become one of the most frequently discussed aspects for achieving cohesion and, at the same time, one of the challenges for EU policies. The ‘territorial dimension’ determines many socio-economic problems and presents challenges for the European Social Fund (ESF), which has to enhance its flexibility and highlight the capacity and needs of specific territories at national, regional and local levels at the programming and implementation stages. While our understanding of the national and regional levels has advanced, the dynamics with the local level need further consideration, chiefly in the context of Europe 2020 strategy, and regarding the territorial dimension of the European Social Fund and mechanisms of territorialisation. This paper discusses the conceptualisation of territoriality and the different levels of applicability in regional development approaches. The paper draws on OECD and other organisations research and analysis; particularly the work of the OECD Local Economic and Employment Development Programme (LEED). The paper argues that the local level is emerging as the key spatial dimension where EU development instruments apply and therefore a systemic local approach may be needed when designing national and regional cohesion policies and instruments. The paper is divided into 5 sections discussing: 1) The importance of an integrated spatial approach to development; 2) The success of the local approach to development: complexity, integration and the policy mix; 3) Integrating territorial mechanisms for job creation, employability and inclusive growth; 4) Fostering education policies for qualification and skills rich ecosystems; and 5) The way forward.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".