Cultural counterfactuals: assessing the impact of Indigenous social procurement in Australia
Bibliographic record
Abstract
In countries like Australia, Canada and South Africa with large Indigenous populations, governments are increasingly turning to social procurement to solve entrenched social problems like Indigenous disadvantage. Social procurement works by leveraging construction and infrastructure spending to encourage construction firms to give back to the communities in which they build. It does this through new partnerships with governments, not-for-profits and social benefit organisations like Indigenous enterprises, which deliver construction products and services in ways that, benefit Indigenous communities. However, the success of social procurement policies is typically judged from an outsider's perspective, ignoring Indigenous notions of value: the intended beneficiaries whose lives social procurement is aimed at improving. Mobilising strain theory and undertaking a critical literature review to conceptualise social procurement in a new way, this paper explores the proposition that current methods of assessing the success of Indigenous social procurement. It finds that policies are culturally insensitive and fail to articulate adequately their social impact on the communities they are designed to benefit, presenting an overly optimistic view of success that does not align with Indigenous perspectives of social value. We also argue that the project-based nature of construction appears to conflict with Indigenous notions of social value by undertaking temporary endeavours that lack local knowledge. The paper concludes by presenting a new conceptual framework of cultural counterfactuals that will allow future policy social impact assessments to represent better the views of Indigenous people in the social procurement policy debate.
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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 | 0.049 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".