Beyond participation and distribution: a scoping review to advance a comprehensive justice framework for impact assessment
Bibliographic record
Abstract
Industrial projects bring about dramatic social change. With the Impact Assessment Act 2019 there is a greater emphasis on the social impacts of development and on the “meaningful participation” of citizens in impact assessment (IA). It is widely believed that meaningful participation can improve the legitimacy of development and even provide a step towards reconciliation with Indigenous peoples, which is a commitment set out explicitly in IAA 2019. To foster meaningful participation and deliver sound decision-making, impact assessments must also be just. Calls have been made for integrating justice more centrally in impact assessment practice and evaluation but work is needed to inform just IA processes. Our report draws on a framework of justice that emerges from environmental justice (EJ) scholarship and activism and defines justice along three interdependent dimensions: distribution, representation, and recognition. Our report tests the hypothesis that there is a gap in research which addresses all three dimensions of this justice framework, and we assess how this gap might translate into a gap in methods for guiding meaningful participation in IA. The objective of this report is (1) to provide an overview of the ways in which existing approaches to IA address EJ, and (2) to outline what an EJ approach to meaningful participation in Canadian federal impact assessment would entail in practice. Based upon a scoping review of 593 academic articles, 20 technical reports and government documents, and 2 blogs/media articles, we conclude that articles which address justice in IA typically focus on either its distributional or procedural dimensions. We recommend (1) future research focusing on recognitional justice as this will be helpful for Canadian IA policy and practice where issues of Indigenous sovereignty and claims for self-recognition are front and centre in IA decisions (and disputes over them); (2) that Canadian best practices be synthesized for practitioner and decision-making communities, and that future scholars integrate IA practice; and (3) that scholarship explore the trade-offs of privileging the nation-states’ duty to decide in the broad public interest, on one the hand, and principles of EJ that emphasize community-driven problem definition and decision-making, on the other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".