Bibliographic record
Abstract
Canadian historians often name well-known American War of Independence (1775–1783) as the First American War, because military activities in Canada waged not Canadians, but English and American armies. It was the result of “information mistakes” of American insurgents. However, its gave the paradoxical desire of Canadian Catholics to remain subjects of ardent protestant George III and the foundation of the “Montreal Gazette” (1778) by Fleury Mesplet, the representant of American Continental Congress. Relevance of this historical event research consists in its lessons for the present time. There aren’t special researches on the First American War “information campaign”, however some documental sources were published in Canada. So, we used the content-analysis of these materials as the main method of research. The aim of our study is analyzing American “information campaign” failure in Canada. The hypothesis of research: successful information support of military activities in elsewhere territory is not possible without taking into consideration its regional especial features. The key aspect of the present research problem: if population of seizing territory has another language, faith, cultural and historical values, social and economic structure, the foreign invasion must aspire to constructive “information dialogue”. The results of this study include generalization of the “information events” of the First American War. In discussion, we note changed interpretation of the war not only by Canadians but also by some American leaders:from positive to negative perception. The present article is the first Russian experience of the First American War “information content” study, that gives the wide perspectives for its further research.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".