Reframing the Conversation: Understanding Socio-Economic Impact Assessments within the Cycles of Boom and Bust
Bibliographic record
Abstract
Socio-economic impact assessments (SEIA) are an essential step in identifying and evaluating the potential direct or indirect impacts of proposed economic developments, programs, and projects on communities. A SEIA provides a framework to address the changing demographics and the impacts on services and infrastructure, economic structures including employment and business opportunities, quality of life, the overall health and well-being of the population, and the cultural values of the community. Communities that host rapid industrial development often follow a predictable trajectory of rapid development, operational stability, and industry decline. This cycle known as the "boom and bust" cycle has significant implications on the sustainability of communities. With British Columbia on the cusp of significant industrial development, the Health Officers Council (HOC) of British Columbia in partnership with Northern Health, hosted a collaborative workshop to strategize about addressing the socio-economic impacts of proposed developments on communities. Through collaborative and open dialogue, collectively the group identified the key strategies in supporting to communities to maximize the benefits of development while mitigating the potentially negative socio-economic impacts.
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 | 0.062 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.027 | 0.071 |
| Scholarly communication | 0.030 | 0.055 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 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".