Valuing terra nullis: Dealing with the impact of pipeline and infrastructure projects in the Arctic
Bibliographic record
Abstract
Until the twentieth century the northern regions of Russia and North America had little by way of infrastructure and urban development. This changed as the extent of the mineral wealth in these regions came to be appreciated and exploited. During the Second World War and the Cold War the regions came to be of strategic importance, resulting in the building of major military bases, such as the Thule Air Base, and infrastructure, such as the Alaskan Highway. The mineral wealth of the region, particularly hydrocarbons, is located long distances from consumers, requiring major infrastructure projects across challenging terrain and environmentally sensitive areas. The beneficiaries of such projects, who gain access to more secure and cheaper energy sources, often reside a long way from where the impact of the projects is felt. By contrast, the local population may have to bear the environmental and social consequences and any adverse impact upon traditional livelihoods, such as hunting, fishing, and nomadic agriculture. These raise questions about how and whether the local populations can share in the benefits from development and the extent to which the environmental and livelihood consequences can be mitigated whilst achieving the understandable desire of central governments to improve living standards and security of energy supplies for their populations as a whole. In the early days of development in the region, there was a tendency on the part of governments to view the region as terra nullis, wilderness land belonging to no-one. In lands without settled agriculture, surely those displaced from one piece of tundra, taiga, or icecap to another could be relocated on an apparently interchangeable land? This was done without regard for the extent to which the landscapes were managed, the impact of relocation and development on hunting and other activities or the ties of indigenous populations with the land. More recent developments have provided from greater participation of the local population in the planning process and a sharing of the benefits from development. The paper compares and contrasts the approaches adopted in North America, particularly Canada, and the Russian Federation.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".