Lessons from Canada for green procurement strategy design
Bibliographic record
Abstract
Abstract We derive lessons for green public procurement (GPP) by examining it in the context of Canadian federal government expenditures in several sectors. These show that successful GPP is neither simple nor automatic but requires alignment of green policy visions between payers, purchasers and producers, and the existence of appropriate procurement frameworks to allow this alignment to persist. Attaining and maintaining this alignment longitudinally is especially difficult as priorities, and governments can change over time, ‘de-aligning’ any initial agreement on the merits of the strategy behind ‘strategic procurement’ of any kind. While less acute for short-term procurement, this problem exists for many longer-term green procurement projects and can lead to government attempts to downplay long-term efforts and seek less complex short-term purchases where alignment is easier to establish and maintain but where green efforts may be less impactful. These dynamics are illustrated in the case of green procurement efforts made in Canadian federal programmes including the little-examined but important defence sector.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".