Socially Responsible Public Procurement and Set-Asides: a Comparative Analysis of the US, Canada and the EU
Bibliographic record
Abstract
Public procurement can be used to achieve goals other than purely economic ones. Such goals are often referred to as “social linkages”. A preference for social considerations has been gaining ground against the dominant best value for money (BVM) paradigm over the past few decades. In the past, public procurement policies followed the principle of non-discrimination and free competition beyond national boundaries. Today considerations other than (purely economic) BVM have become relevant in public procurement policy and practice. Examples of social linkages in public procurement are found in various countries, from the well-known ‘Affirmative Action Programs’ in the US that advance minorities, women, persons with disabilities and veterans,1 to specific set-aside programs made available to only less-competitive businesses, such as women-owned businesses, minority-owned businesses, businesses operating in economically disadvantaged areas, etc. Set-asides can be seen as social procurement linkages through the promotion of both supplier diversity and employment. The latter means that social use of public procurement can positively impact employment by providing opportunities to workers who are generally excluded from the labour market, while the former means that chances are given to less-competitive bidders. Set-aside programs have been widely developed in the US, which has a long tradition of set-aside contracts for special classes of small businesses, including small disadvantaged businesses, and in Canada where set-asides have been introduced for the development of Aboriginal businesses. However, the restriction of full and open competition that set-asides entail is frequently criticized by EU institutions. Despite this, the new European procurement framework also seems to have established set-asides as a means of providing economic opportunities to disadvantaged groups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".