Bibliographic record
Abstract
Nearly 85% of the EU’s public procurement market is open to third country bidders. Many European businesses, although they face third country competition in the internal market, cannot access procurement markets overseas. As a result of the increasing competition from third country suppliers, which is believed to create an uneven playing field in the internal market, the Commission has issued guidance on the participation of third country bidders in the EU procurement market. To ensure a fair and level playing field, the Commission promotes two approaches: first, the wider application of the available instruments in EU public procurement law (sustainable public procurement and abnormally low tender rules), to ensure that the same, or equivalent, requirements apply to both EU and third country suppliers, and, second, the introduction of the reciprocity regulation (the “International Procurement Instrument”, or IPI). If the IPI is introduced, the rules that apply to EU businesses will be different from those that apply to businesses from third states.T his contribution concludes that, since 2012, the political will amongst Member States has not been strong enough to approve the IPI. However, the existing EU public procurement rules allow the playing field between EU and third country suppliers in the internal market to be made level. Nonetheless, the political will, the required skillset and capacity within the public sector are necessary
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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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.022 | 0.038 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".