fraserinstitute.org FRASER RESEARCH BULLETIN 1
Bibliographic record
Abstract
Alberta’s new government has implement-ed, or plans to implement, many policy changes that will affect the oil and gas sector. Some of these changes include increases to the corpo-rate income tax (CIT), increases in the carbon levy, and a panel review of the province’s oil and gas royalties and climate change policies. The 2015 edition of the Global Petroleum Survey was conducted from May 29, 2015 to July 31, 2015, presenting a unique opportunity to assess how Alberta’s policy changes have af-fected investor confidence. Alberta experienced a large negative shift from 2014 to 2015 in the Global Petroleum Sur-vey. On the Policy Perception Index, a compre-hensive measure of the extent of policy-related investment barriers within each jurisdiction, where a high score reflects negative senti-ment on the part of respondents and indicates that they regard the jurisdiction in question as relatively unattractive for investment, Alberta’s score deteriorated from a value of 26.57 in 2014 to 34.21 in 2015. The province’s rank in 2014 was 16th (of 156 jurisdictions), deteriorating to 38th (of 126) in 2015. The investment driver that experienced the largest shift in negative sentiment from 2014 to 2015 was political stability. In 2014, only 5 % of respondents viewed political stability as a de-terrent to investment; that increased to 51 % of respondents in 2015. Another large negative shift was in the fiscal terms policy category, which includes the roy-alty framework. In 2014, only 14 % of Alberta’s respondents found this factor to be a deterrent to investment; that rose to 39 % in 2015. These negative shifts may not bode well for Alberta considering that the province’s immedi-ate geographical competitors are perceived to be either attractive jurisdictions to invest in, or are improving.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.709 | 0.505 |
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".