Sustainability, Public Procurement and SMEs - Challenges and opportunities
Bibliographic record
Abstract
The global value of public procurement spending is enormous. Each year, approximately 19% of the EU GDP is spent by over 250,000 public authorities purchasing services, works and supplies. The sheer scale of public procurement spending and supplier selection decisions can literally create and shape the market, impacting the lives of citizens across countries and regions at large. Over the last decade public procurement experienced wide spread modernization across the globe, both including local and international levels. These days, public procurement is no longer just about buying the cheapest possible supplies or services but, rather, it is understood as a process whereby organizations meet their needs in a way that achieves value for money on a lifetime basis and delivers aspects beyond savings. This includes doing business responsibly, taking a leadership position in the community and ensuring promotion of Small and Medium Enterprises (SMEs) in public procurement. SMEs are considered the backbone of the European economy as they represent around 99,8 percent of all enterprises and produce more than a half of the European GDP. Thus, if more SMEs participate in public procurement, the competition will increase and, consequently, governments will be able to achieve better value for money in their contracts. Also, it has been argued that most innovations originated from small entrepreneurial firms which support the sustainability agenda. The main claim of this article is that sustainability and the promotion of SMEs participation in public procurement do not necessarily contradict each other. It is without doubt that SMEs participation in the procurement context encompasses a number of challenges. To the latter may be accounted the argument that SMEs are too small, have too little money, experience or technical capabilities to participate in public procurement. Yet, it is argued that revised procurement law has the potential to solve some of the challenges and there is still considerable scope for promoting opportunities for SMEs such as reliance on the capabilities of third parties, bidding in form of consortia or subcontracting, the possibility to influence contract specification, and reserved contracts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".