Bibliographic record
Abstract
Improving energy efficiency is increasingly recognized as key to the energy transition and offers numerous benefits at the household level. There has been little scholarship to date addressing how energy efficiency is governed, and even less study of how Indigenous Nations and settler governments might co-develop energy efficiency policy. This gap is notable because energy efficiency policy can exacerbate equity issues when done without proper consideration of the diverse needs of different energy users. Through a document analysis and key informant interviews, this case study of energy efficiency governance in Manitoba, Canada investigates how Efficiency Manitoba works with Indigenous Nations and how well its programs meet the needs of Indigenous communities. Efficiency Manitoba’s approach to building relationships with Indigenous Peoples has several strengths that other settler governments and institutions would do well to emulate. Evaluating the overall success of Efficiency Manitoba’s programs for Indigenous participants is inhibited by limited data availability, so there is a need for better measurement and transparency. This research also found that the overlap between housing policy and residential energy efficiency policy is much greater than is commonly acknowledged in energy efficiency literature. Efficiency Manitoba is already collaborating with Indigenous-led housing initiatives, but more explicit integration of energy efficiency and housing supports could be beneficial, especially on First Nations reserves.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".