'IAIA14 Conference Proceedings' Impact Assessment for Social and Economic Development
Bibliographic record
Abstract
Socio-economic monitoring plans are designed to facilitate issues tracking and management regarding the intended and unintended impacts and benefits of major projects. They also provide a feedback mechanism to the socio-economic assessor and proponent, which can introduce greater certainty to future socio-economic assessments and inform proponent initiatives on future projects. The aim of this paper was to identify the extent to which socio-economic monitoring is utilized in Canadian pipeline projects, identify lessons that can be learned from socio-economic monitoring in other resource extraction industries and geographies, as well as make recommendations on the application of socio-economic monitoring for the Canadian pipeline industry. The current Canadian legislative framework was reviewed for socio-economic monitoring requirements. Other Canadian industries, North American and international examples were also examined to provide context on best and emerging practices related to socio-economic monitoring. This paper also examines whether recent major pipeline projects have proposed and/or implemented socio-economic monitoring plans. This paper finds that, while socio-economic monitoring is utilized to some extent within the mining industry, there is limited known application within the Canadian pipeline industry. Lessons and strategies that can be applied to the Canadian pipeline industry and regulators are discussed. Ultimately, this paper argues that the diverse nature of the Canadian socio-economic landscape and the linear nature of pipelining support the need for socio-economic monitoring plans that will track and respond to the varied interests of stakeholders and the dynamic nature of socio-economic outcomes. Socio-economic monitoring can be an important tool for managing non-technical risk.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.025 |
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".