Bibliographic record
Abstract
The intensity of the people’s role in military activity has changed from the days of mass mobilization war that characterized the West in the 19th and first half of the 20th centuries. 1 When, for instance, NATO armies are deployed, the populations of most of Europe and North America may ‘sympathasize but do not suffer; they empathize but they do not experience’. 2 However, that is not to say that the people have no role in contemporary operations. In democracies like Canada, the population has a significant influence on the way both the government and the military conduct themselves on the battlefield. So while Colin McInnes may be correct in his assessment on suffering, he is wrong when he says that “society no longer participates, it spectates from a distance.” 3 Philip Everts believes that, “whether the consequences are good or bad, and whether we like it or not, the public is … always involved in wars, their participation, conduct or prevention, and whatever their form, as participant or observer.” Therefore, he continues, “public opinion, what people think and the way they look at the world and how they act upon their convictions in the political process [is] not only a topic of concern to governments, but consequently also a major factor in understanding foreign policy and international politics.” 4 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".