Employing qualitative methods for assessing impacts of major projects in Canada
Bibliographic record
Abstract
The need for broader consideration of sustainability outcomes has been leading the discourse in the field of impact assessment during the twenty-first century. The ability of qualitative methods from the social sciences are being proposed as having the potential to integrate different types of knowledge in order to identify, explore, and assess impact pathways that address more social sustainability concerns around development such as values, culture, well-being, psychosocial impacts, rights, and cumulative effects. The purpose of this research was to explore how qualitative methods in the Canadian assessment context can contribute to this next generation of impact assessment. As such, my thesis undertakes to identify examples of qualitative methods being used in Canada and to explore how some of them are being applied in a case study context. It also identifies broad challenges and barriers to using qualitative methods and explores how these might be addressed using good practice. I employed semi-structured interviews with practitioners and document review from three proposed projects, the Teck Frontier Mine, BC Hydro’s Site C Clean Energy Project, and Benga’s Grassy Mountain Mine. I found that key qualitative methods being used in the Canadian context included interviews and focus groups. I found that there were important design considerations in IA for using qualitative methods, particularly in community-based research contexts, such as sampling, methods protocols, interpreting human experiences reflected in data, and qualitative data analysis. Key institutional challenges that limited the application of qualitative methods into assessment included formal institutional processes that struggled with integrating qualitative data, guidance on implementing, decision-making using qualitative data as evidence, and other logistical and political challenges. In conclusion, careful design, good guidance, and collaborative process are key to grounding qualitative methods to assessment and decision-making outcomes.
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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 | 0.050 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".