Planning Development, Promising a Better Future Through Infrastructures: The Cases of Fort St. John, Prince Rupert, and Kitimat in British Columbia
Bibliographic record
Abstract
The lure of development, intertwined with promises of creating endless growth, well-being and socio-economic opportunities, has been used in British Columbia to shape a specific narrative around resource exploitation while justifying the continued approval of development projects. Pipelines such as the Coastal Gas Link (CGL) or LNG liquefaction facilities in Kitimat have been approved and praised as infrastructures that can bring prosperity to locals while fostering the global green transition by shipping “clean” gas and resources to Asia, by using the two deep-water, ice-free ports of Kitimat and Prince Rupert, located in Northwestern British Columbia. Often presented as the shortest routes to link North America to Asia; the former provides the fastest and most cost-effective route for LNG export through the Douglas channel, while the latter is believed to offer the best options for shipping goods into North America while exporting raw materials and resources to growing Asian markets. The discourse around the necessity of such infrastructures has revamped since Donald Trump took office as the 47th president of the United States on 20 January 2025. The recent tariffs imposed by the US on Canadian goods and the ongoing threat to Canadian sovereignty provide industries and financial actors with a strong argument to foster the discourse around the necessity of such infrastructure, with politicians using it to shape Canada’s 2025 federal election campaign. Combining all these elements, by engaging with the literature on infrastructure and drawing on my fieldwork experience, this contribution explores how infrastructures have been used to shape and strengthen the narrative around the perpetual need for further development while highlighting the impact infrastructure development has had on people’s daily lives and their ability to envision the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".