Sea Power and the Canadian Military-Naval Industry (2010-2020) | Poder Naval e a Indústria Militar-Naval Canadense (2010-2020)
Bibliographic record
Abstract
Due to the historical difficulty of high investments in periods of conflict and large cuts in periods of peace, the Canadian military-naval industry and the country's Navy suffered from a lack of infrastructure, low technological development in some areas and scrapped equipment. After years of instability, the Harper government launched in 2010 the National Shipbuilding Strategy in an ambitious attempt to modernize the Navy and Coast Guard. Thus, the main objective of the article is to understand the role of the State as a relationship promoter within this sector and its influence on the country's naval power. The analysis is based on the Structure-Conduct-Performance (SCD) model. The research uses academic articles, documents and official reports, and is limited to 2010-2020, based on the launch of the strategy that was a milestone for the industry. Keywords : Military-Naval Industry; NSS; SCP Paradigm. RESUMO Por uma dificuldade histórica de altos investimentos em períodos de conflitos e grandes cortes em períodos de paz, a indústria naval militar canadense e a Marinha do país sofrem com falta de infraestrutura, baixo desenvolvimento tecnológico em algumas áreas e equipamentos sucateados. Após anos de instabilidades, o governo Harper lançou, em 2010, a National Shipbuilding Strategy numa ambiciosa tentativa de modernizar a Marinha e a Guarda Costeira. Dessa forma, o principal objetivo do artigo é entender o papel do Estado como promotor das relações dentro desse setor e sua influência para o poder naval do país. A análise é baseada no modelo Estrutura-Conduta-Desempenho (E-C-D). A pesquisa utiliza artigos acadêmicos, documentos e relatórios oficiais, e tem como delimitação temporal 2010-2020, baseado no lançamento da estratégia que foi um marco para a indústria. Palavras-chave : Indústria Naval-Militar; NSS; Modelo E-C-D. Recebido em: 9 nov. 2020 | Aceito em: 10 out. 2021.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".