A Proposal to Create a Pan-Canadian Energy Information Organization (CEIO)
Bibliographic record
Abstract
Canada is a safe and stable resource-rich nation in an increasingly energy-hungry world. While this state of affairs imbues our riches with strategic importance, it also creates an acute need for accurate data collection guided by nationally accepted methods, tools and approaches, to cut through the tangle of overlapping jurisdictions that confuse present attempts to understand the Canadian energy sector as a whole. Prepared at the request of the Alberta Department of Energy, this paper proposes the creation of the Canadian Energy Information Organization (CEIO), an independent, objective energy information agency similar, but not identical, to the Energy Information Administration in the United States and the International Energy Agency serving OECD member countries. Funded through modest provincial contributions and working with Statistics Canada, the CEIO would support federal and provincial energy regulatory mechanisms; offer timely energy forecasts, analysis and statistical interpretations; lower research costs for the provinces; promote clear and uniform reporting standards; and aggregate facts and figures in an easily accessible database functioning as an official information portal, educating the public and ensuring that Canada makes the most of its energy bounty. A nationally recognized authority on the Canadian energy sector is long overdue, and in sketching one (right down to the level of corporate governance, budgeting and staffing), this paper fills in a major gap in Canada’s energy landscape.
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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".