Assessing the impact of social procurement policies for Indigenous people
Bibliographic record
Abstract
Governments of highly developed western nations with colonised Indigenous populations such as Australia, Canada and South Africa are increasingly turning to social procurement policies in an attempt to solve social inequities between Indigenous people and other citizens. They seek to use policies and funds attached to infrastructure development and construction to encourage private sector companies to provide training, employment and business opportunities for Indigenous people in the communities in which construction occurs. This paper outlines the rise of these policies and their origins, and critiques their connection to Indigenous people’s human rights, impact measurement, evaluation and accountability mechanisms. In doing so this paper also explores benefits and potential of social procurement policies, as well as risks. Drawing on insights from an Aboriginal-developed evaluation framework, Ngaa-bi-nya, and Indigenous Standpoint Theory, this paper highlights Indigenous peoples’ definitions of value and outlines their relevance to social procurement. Introducing the notion of cultural counterfactuals into social impact measurement research, it also offers a new conceptual framework to enable policymakers and practitioners to more accurately account for social procurement value and impact, including Indigenous people’s notions of social value.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".