Indigenous Procurement as a Catalyst for Community Building
Bibliographic record
Abstract
From 2018 to 2021, a series of Indigenous Procurement Engagement sessions (IPE-sessions) took place in-person and virtually in Ottawa and Toronto to explore the modernization of Indigenous procurement in Canada. Stakeholders from regional and national Indigenous organizations, Indigenous and non-Indigenous business leaders in the private sector, as well as federal government officials, participated in the engagement sessions. In total, there were 98 participants (n = 98) for all the engagement sessions (28 in 2018; 49 in 2020; and 21 in 2021). This research re-analyzes data collected from 2018 to 2021 and aims to answer the question—can Indigenous procurement be a catalyst for community building? The research re-analyzes the data through the exploration of 4 main chapters: 1) Building Strong First Nations Economies: Economic Development, Community Building, and Procurement; 2) Social Procurement Policy and the Inclusion of Diverse Supply Chains. Is Indigenous Procurement ‘Social Procurement’? 3) Challenges and Wise Practices for First Nations Procurement in Canada; and 4) Should Indigenous Procurement be Legislated? Federal Indigenous Procurement Policy Versus Article 24 of the Nunavut Agreement. The research findings indicate that procurement is a catalyst for First Nations community building as local procurement contributes to community prosperity through business development and growth, job creation, and community wealth building, as well as other social outcomes, which are defined by First Nations communities, organizations, and businesses.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".