Informing public interest determinations in impact assessment using a multiple account evaluation framework
Bibliographic record
Abstract
This article presents a comprehensive multiple account evaluation (MAE) framework that is intended to inform public interest determinations in impact assessment (IA). Using MAE methodology; which involves separating impacts into government revenue, economic activity, environmental, social, health, and Indigenous accounts; the proposed ‘Public Interest MAE Framework’ seeks to inform senior government decision makers on all the positive and adverse consequences associated with a proposed project in a manner that allows for analysis of key trade-offs from the perspective of society as a whole. The proposed framework is applied to a case study to demonstrate how the framework functions in practice. Additionally, a survey is conducted with IA practitioners, experts, stakeholders, and Indigenous groups to evaluate the proposed Public Interest MAE Framework. The primary conclusion of this study is that the Public Interest MAE Framework has the potential to inform public interest determinations and overcome many of the limitations associated with other estimation methods used in IA. Finally, opportunities and challenges associated with integrating the Public Interest MAE Framework into IA are explored.
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 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 | 0.146 | 0.131 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".