Active Inclusion and Industrial Relations: Regional Case Studies (Work Package C)
Bibliographic record
Abstract
Airmulp (Active inclusion and industrial relations from a multi-level governance perspective) focuses on the relationship between active inclusion and industrial relations. More specifically, the Project is concerned with the analysis of active inclusion issues – e.g. social exclusion, in-work poverty, labour market segmentation, long-term unemployment and gender inequalities, income support and inclusive labour markets – in the framework of social dialogue and collective bargaining, at three different levels, namely European, national and sub-national (regional and/or local). WP C focuses on the relationship between active inclusion and industrial relations at the sub-national level. The devolution of competences related to active inclusion to either regional or local governments, together with the decentralisation of industrial relations, enlarges the room for manoeuvre of social partners. Despite this trend, there are few studies on the dynamics of industrial relations at these levels, even if the regional dimension of labour market and social integration plays an important role in many European countries. However, the institutional architecture underpinning the territorial dimension of industrial relations varies across countries, and it is unclear if this masks the existence of shared, cross-national principles framing industrial relations at the decentralised level. This WP proposes a cross-national comparison of practices undertaken at the territorial level. The results are intended to deepen knowledge of the role and efficacy of the sub-national regimes of labour regulation across Europe, in relation to active inclusion. The research is based on six case studies, selected among larger regions which contain a “second-tier” city. These are: Rhône-Alpes and Lyon (France); Lombardy and Milan (Italy); Catalonia and Barcelona (Spain); Lower Silesia and Wroclaw (Poland); West Sweden and Gothenburg (Sweden); Greater Manchester and Manchester (UK). The analysis, then, has the purpose of revealing common features and diversities between the case studies as well as regional specificities within the selected countries. Furthermore, it aims at identifying different approaches to active inclusion, outlying their strong and weak points, and evaluating their outcomes.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".