Qualitative Methods for the Next Generation of Impact Assessment
Bibliographic record
Abstract
Sustainability-oriented IA moves beyond a primary focus on biophysical impacts to consider a broader range of potential social, health and well-being, economic, cultural, cumulative, and equity implications of proposed projects. Canadian IA under the IAA (2019), for example, now explicitly requires consideration of health, social, and economic issues; consistent use of gender-based analysis plus (GBA+); evaluation of contributions to sustainability; bridging of Indigenous and Western scientific knowledge; and meaningful public participation. Quantitative methods are typically used to examine cause and effect associated with biophysical impacts and to identify, for example, alternatives and mitigation measures. Delivering effective IA within the broadening scope of next-generation, sustainability-oriented IA, however, requires new thinking and effective methods that enable meaningful inclusion of diverse knowledges, values, and information sources. For many of the broader range of impacts considered in next-generation, sustainability-oriented IA, cause and effect can only be established—and alternatives and mitigation measures suggested—through qualitative methods that can explain the values and connections people have with the places and land where projects are proposed. While this report is primarily intended for those involved in Canadian IA, the project was implemented by an international project team and informed by experts around the globe. Therefore, we anticipate this report will also be relevant to those working in a range of IA systems and geographical contexts. Specifically, this report may be of interest to: • practitioners working for/with communities and project proponents to gather the best possible information about the potential implications of proposed developments; • decision makers with a role in evaluating and synthesizing the information received throughout an IA process; • researchers who are testing, critiquing, and pushing the boundaries of IA processes and methods; • educators fostering the upcoming generations of IA professionals; • communities and members of the public who (should) play a role in selecting and implementing the methods that best tell their stories of place, change, and impact. There is considerable opportunity for the continued integration of qualitative methods in IA, but there are also barriers that often make it difficult to implement these methods in practice. While this report presents a range of conventional, innovative, and participatory qualitative methods (17 methods categories in total), it also discusses the barriers that must be overcome if these methods are to be effective in the context of sustainability-oriented IA.
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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 | 0.205 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".