Bibliographic record
Abstract
The purpose of this report is to describe a framework for sustainability-based decision making; establish the public interest and legislative basis for undertaking sustainability-based assessments, or their substantive equivalents, in Manitoba; and assess whether there are grounds for confidence that the proposed Keeyask project, as described in the Response to the EIS Guidelines, will promote progress towards sustainability while avoiding significant adverse effects. Sustainability assessment is an integrated approach to decision making that centres upon clearly establishing a need (in this case for the services provided by electricity) through an open and democratic process, providing a fair and full assessment of alternatives, and assessing the alternatives against an explicit set of sustainability criteria that have been specified for the particular case and context. In the context of the proposed Keeyask hydro dam, applying a sustainability assessment framework is necessary to ensure long-term improvement in human and natural welfare. Whether, it is climate change, biodiversity, declining resources, or threats to traditional ways of living, the proposed dam touches upon many critical issues of the 21st century. Furthermore, the significance of the near term and legacy effects of the proposed dam makes it imperative to fairly share impacts and benefits both within and between generations. Within the province of Manitoba, the combination of the Manitoba Sustainable
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.377 | 0.302 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".