Where in the World are Canadian Oil and Gas Companies? An Introduction to the Project
Bibliographic record
Abstract
In April 2013, The School of Public Policy formally launched the Extractive Resource Governance Program, a platform to harness Canadian and international research and technical expertise to assist resource-rich jurisdictions in establishing sustainable and mutually beneficial policies for governance of the extractive sector. The program delivers applied policy research, technical assistance and executive training programs to countries with emerging or established extractive resources, working in collaboration with governments, regulatory bodies, academia, civil society, and industry. Begun in 2011 as an internal research tool for the development of the Extractive Resource Governance Program, this project was conceived as a means to identify jurisdictions where Canadian companies had ongoing projects and activities around the world. This paper introduces the methodology used to answer the question: Where in the world are Canadian oil and gas companies? To answer this question, firm-level data from publicly traded Canadian companies were collected and analyzed culminating in the development of an online tool for public use. This paper accompanies an interactive website launched by The School’s Extractive Resource Governance Program and describes the data available online as well as in the annual reports released by The school. The website and annual reports allow interested users to geographically locate jurisdictions around the world where publicly traded Canadian oil and gas companies have activities, over time. The website is available at http://www.policyschool.ca/research-teaching/teachingtraining/extractive-resource-governance/ergp-map/. While Canada is a well-recognized oil and gas jurisdiction within its own borders, the extent of activity that Canadian companies undertake in the international arena is less well known. For instance, while Natural Resources Canada collects and publishes regular data on Canadian mining assets and activities abroad, it does not do so for the oil and gas sector. Statistics Canada collects information about Canadian direct investment abroad (CDIA)1 in the energy sector, but for the purpose of answering the question posed in this paper, these numbers can be somewhat misleading, as CDIA data solely tracks the first destination of Canadian investment rather than the final destination of investment (which can often be different).2 Frequently, oil and gas companies (like others) use international financial centres to conduct their business operations as part of their global value chain. This can prove problematic when seeking to identify sector-specific data on the final destination of investment. For instance, one of the challenges in using CDIA statistics is the existence of so-called tax-haven countries such as Barbados and the Cayman Islands. Tax-haven countries are low-tax jurisdictions that serve as conduits to the global economy.3 While the capital investment of a Canadian company can initially arrive in a tax-haven country, frequently the investment is ultimately bound for a third-market destination, for instance one in Latin America or the United States.3 The use of tax-haven countries as conduits in financing outbound investments distorts CDIA statistics, making it difficult to use these data to determine the presence of Canadian oil and gas companies around the globe. This paper provides a comprehensive overview of the methodology used in the collection of data for the Where in the World (hereafter WIW) project. It begins by presenting the definition of a Canadian oil and gas company (O&G) within the context of the WIW project, followed by a description of the types of O&G companies considered in the analysis. It also provides a description of the data sources used in the extraction of financial and operating statistics, and outlines the various types of data used to determine the scope of O&G activities of Canadians companies abroad.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".