Case C-598/19 Conacee – Reserved Contracts and Additional National Criteria Beyond Those Laid Down in Article 20(1) of Directive 2014/24/EU on Public Procurement
Bibliographic record
Abstract
In this blog post, the authors analyse Case C-598/19 Conacee, which concerns Article 20(1) Directive 2014/24/EU on reserved contracts and the regulation foreseen in the Fourth Additional Provision and the Fourteenth Final Provision of Spanish Law on Public Sector Contracts, setting additional requirements at the national level for the economic operator to compete for reserved contracts. [1] The Court of Justice of the European Union (further: CJEU) ruled in this case that Member States are able to set additional requirements under national legislation beyond those laid down in Article 20(1) Directive 2014/24/EU with respect to economic operators that may compete for reserved contracts. It also clarified the conditions under which Member States may exclude economic operators from the reserved contracts. The blog post first examines relevant EU law provisions, facts of the case and the question submitted to the CJEU. Next, analyses of reserved contracts under Article 20(1) of Directive 2014/24/EU and the judgement in Conacee are carried out. The latter is investigated with particular attention to methods of interpreting the legal text and distinguishing between non-profit and profit special employment centres. The last section concludes the blog post.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".