Smart Public Procurement and Labour Standards:Pushing the Discussion after RegioPost
Bibliographic record
Abstract
Smart procurement aims to leverage public buying power in pursuit of social, environmental and innovation goals. Socially-orientated smart procurement has been a controversial issue under EU law. The extent to which the Court of Justice (ECJ) has supported or rather constrained its development has been intensely debated by academics and practitioners alike. After the slow development of a seemingly permissive approach, the ECJ case law reached an apparent turning point a decade ago in the often criticised judgments in Rüffert and Laval, which left a number of open questions.<br/><br/>The more recent judgments in Bundesdruckerei and RegioPost have furthered the ECJ case law on socially-orientated smart procurement and aimed to clarify the limits within which Member States can use it to enforce labour standards. This case law opens up additional possibilities, but it also creates legal uncertainty concerning the interaction of the EU rules on the posting of workers, public procurement and fundamental internal market freedoms. These developments have been magnified by the reform of the EU public procurement rules in 2014.<br/><br/>This book assesses the limits that the revised EU rules and the more recent ECJ case law impose on socially-orientated smart procurement and, more generally, critically reflects on potential future developments in this area of intersection of several strands of EU economic law.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".