Myths and Realities about Petroleum-Related Development: Lessons for British Columbia from Atlantic Canada and the North Sea
Bibliographic record
Abstract
The government of British Columbia has recently agreed to lift the moratorium on offshore petroleum exploration on Canada’s west coast, and the federal government has been asked to follow suit. There is a strong possibility, therefore, that drilling for oil and gas offshore British Columbia will begin in the near future. Some coastal communities, hard-hit by resource problems in forestry and fisheries with attendant unemployment and out-migration, have been lobbying in favour of lifting the moratorium, while environmentalists and some First Nations people have been lobbying equally as hard for its continuance. Predicated on the assumption that BC may well be on the verge of developing an offshore petroleum industry, this article essay explores several issues related to the development of such an industry. It poses the question: if the province is to develop an offshore oil and gas industry, how should it go about it in as environmentally and socially responsible a way as possible? Fortunately for British Columbia, two provinces on Canada’s east coast, Newfoundland and Labrador, and Nova Scotia, already have established offshore petroleum industries. They have a 40-year history of experiences in both the exploration and production phases of offshore development. This essay provides an overview of the Atlantic Canadian and North Sea experiences. It argues that it is, in principle at least, feasible to manage offshore developments in a socially and environmentally responsible way, and in such a way that local people and communities, including First Nations, can be beneficiaries. There is nothing automatic about this, however. It will require enlightened governance at both the provincial and federal level, and a willingness to devolve to effected communities enough decision-making authority to negotiate with the oil companies from a position of strength. Success will depend on local people becoming well informed about the industry and taking the lead in determining the conditions under which development can proceed.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.059 | 0.025 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".