Cold, Spread Out, Far...and Brilliant! How Native Peoples and Northern Remote Communities Change Transport Planning
Bibliographic record
Abstract
This paper attempts to answer the following questions. Can small and remote local communities influence main decision-makers in transport? Do their specific transport needs and conditions of implementation of transport projects make them only exotic or conversely, do they provide a unique opportunity to learn from them and change our vision about transport planning? This article intends to present the experience of Quebec and more specifically Northern Quebec where collaboration between political decision makers and Aboriginals (Cree and Inuit) remote communities can be regarded as an excellent learning laboratory about sustainability issues and transport governance in general. What can be learned from the situation? In the province of Quebec (Canada) the majority of the population lives in the vicinity of the St. Lawrence River. In contrast, the Northern part of the province has a very low population density. Thus the region of Nord-du-Quebec, the largest of the 17 administrative regions of Quebec (55% of the land area), comprises less than 1% of the total population of the so-called “Belle Province”. Despite this situation, this region has a significant network of land, sea and air transport. However, it is worth mentioning as an example that even today, some northern communities are not linked by land transport infrastructures to the main agglomerations of the south of the province where air and sea transport play a unique role and where all terrain vehicle (ATV) transport acquires a meaning that goes beyond the normally recreational character of this means of transport. Also, contextually, two dimensions make the planning and transport interventions challenging in this specific region. This paper has three objectives. The authors would initially like to offer a quick historical portrait of the transport situation in the remote communities of Nunavik and James Bay over the last 40 years. Second, the authors intend to identify the political, legal, economic and socio-environmental changes that have occurred during this period. Finally, the paper aims to highlight how Native Peoples have contributed to a new vision of transport planning and improved solutions in term of sustainability and access to remote communities. In order to illustrate these changes, the authors will provide 4 concrete examples of projects completed, ongoing and future: (1) the maritime infrastructures of Nunavik; (2) the management and maintenance of local airports; (3) the project of construction of new land access to Kuujjuaq; and (4) the Airfare Reduction Program to isolated communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".