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The Taming of the Skew: Facts On Canada’s Energy Trade

2017· article· en· W6903538038 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueEnergy (signal processing)Energy policyEarningsProductivityNet energyEnergy sectorEnergy sourceOil and natural gas

Abstract

fetched live from OpenAlex

Public perception of Canada’s energy trade is skewed towards Alberta’s oilsands and pipeline projects; a look at the facts reveals a more complex picture. Over the last decade, growth in Canada’s energy trade has been nothing short of historic. Energy exports have become so significant that the revenue is now equivalent to nearly $9,000 for every Canadian household. And it is only projected to grow much, much larger. While Western Canada leads the industry, every region — including Ontario, Quebec and Atlantic Canada — plays a key role. Today, nearly every province is a net energy exporter. The energy sector also adds much to Canada’s economy, with valueadded and productivity higher than nearly every other sector. When it comes to labour compensation, oil and gas extraction is the highest-paying sector in the country, at more than three times the average hourly earnings in the Canadian economy generally, and nearly 50 per cent higher than manufacturing. It is vital that policy debates rely on accurate information; unfortunately, this is not always the case. The often heated rhetoric neglects important aspects of Canada’s energy trade. For example, the type of energy that Canada trades has undergone a dramatic transformation. Ten years ago, natural gas was the largest energy export but today accounts for less than one-tenth of the total. Meanwhile, crude oil exports have more than quadrupled. Even more surprising to many Canadians, and perhaps even policy-makers, is how much energy Canada imports. Even Alberta, with its vast energy reserves, imports a considerable amount of energy. Alberta’s energy imports have grown faster than any other province and will soon exceed Ontario’s, a province more than three times larger with very little of its own oil production. Trade in energy is also intimately tied with Canada’s foreign investment policies. The majority of Canada’s energy trade is in the form of related-party transactions. For example, Suncor exports oil from its Canadian operations to its American refineries to supply its American gas stations. This fact has important implications for Canadian policy: foreign multinational firms are an important and growing part of the country’s rapidly expanding energy trade. Promoting Canada’s energy trade requires lowering investment barriers and creating a predictable and stable investment climate for foreign direct investment. Yet, in practice, Canada has recently shown a tendency for the opposite, with governments blocking the takeover of Potash Corporation by Australia’s BHP Billiton, and announcing, after the takeover of Nexen Inc. by a Chinese firm, that future takeovers would face even greater scrutiny. Foreign investment in Canada’s energy has already begun to fall, feasibly as a result of these increasingly hostile signals. Canada has a great deal riding on the future of its energy industry — an industry that is as economically beneficial as any other, if not more so. It is absolutely crucial that we ensure our energy-trade policies are based on high-quality and objective information; politicized and emotional rhetoric does not help.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.731

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.010
Science and technology studies0.0200.010
Scholarly communication0.0130.006
Open science0.0020.002
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0100.001

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 head0.010
GPT teacher head0.201
Teacher spread0.190 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2017
Admission routes1
Has abstractyes

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