Will It Be DÉJÀ VU All Over Again?
Bibliographic record
Abstract
The boom and bust in energy prices experienced recently has its parallels in the boom and bust of energy prices in the 1970s and 1980s. The earlier boom period saw the Government of Alberta struggle with restraining spending and so became heavily dependent on high energy prices. When in 1986 energy prices crashed the government suff ered a string of large defi cits that was followed by draconian cuts to spending. From 2000 to 2008 the government enjoyed another boom in energy prices and again found it diffi cult to restrain spending. The recent crash in energy prices threatens the government with repea ng the earlier experience of defi cits followed by drama c spending cuts. As it prepares its 2009 budget the government has an opportunity to learn from the past and to quickly and decisively put its budget on a path toward a much smaller reliance on energy-related revenues.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.058 | 0.025 |
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".