Regional development, transport infrastructure and government policy : The case of ice-breaking along the coastline of Norrland, Sweden, 1940–1975
Bibliographic record
Abstract
Due to the cold climate, navigation along the coast lines of the northern regions in Sweden, Finland, Canada, Russia and the United States must negotiate winter conditions which cause ports to freeze over. In order to avoid the negative economic effects of such interruptions, ice-breaking and other measures to facilitate winter navigation have been introduced. This article deals with the introduction of ice-breaking along the coast line of the five northernmost counties in Sweden, the Norrland region, from a perspective that examines and analyzes the underlying decision-making processes. It is concluded that the ability of regional interest groups to link their demands for an improved ice-breaker service to important aims within macro policy such as trade policy, growth policy and regional development policy contributed to the outcome of the decision-making processes. The international competitiveness of the export industries in Norrland was therefore regarded as a national concern during the decision-making processes. Another factor that contributed to the outcome of the decision-making processes was the sectoral organization within the government maritime bodies. Large-scale planning and operational experimentation was allowed to take place within the ice-breaker service, which convinced the government that ice-breaking and winter navigation were a feasible transport alternative.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".