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Record W2959304478 · doi:10.4324/9780429265570-14

The Nep and Energy Policy: an Evaluation

2019· book-chapter· en· W2959304478 on OpenAlexaboutno aff
G. Bruce Doern, Glen Toner

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicHead and Neck Anomalies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomics

Abstract

fetched live from OpenAlex

The partial success is found in the revenue share aspects of the nep. The nep was successful to the extent that it did secure a higher share of revenues for the federal government, from about 7 percent to 16 percent, but short of the avowed goal of about 24 percent, because projected price increases failed to materialize. The full decade of energy policy shows the existence of much consultation, albeit grumpy consultation embellished with the normal expressions of dissatisfaction from those who did not get all that they wanted. The demand-side “off-oil” elements of the nep, aided by the recession and previous conservation efforts, have produced significant results in the form of a 22 percent reduction in oil demand between 1980 and 1983. In other respects, of course, Canada’s earlier energy history showed that there was also at times a dearth of analysis and knowledge, for example about future reserves, industry finances, and procurement decisions.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.038
GPT teacher head0.311
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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