Bibliographic record
Abstract
Abstract The Harper Government's announcement in July 2010 that it would be purchasing sixty-five F-35 aircraft unleashed a storm a storm of controversy. Much of it focused on the anticipated cost of purchasing the aircraft. As the aircraft is still in development the final cost is uncertain. However the focus on costs misses the real debate about the need for the aircraft. What needs to be considered is whether in the future Canada needs the airpower that is provided by the F-35s. As a medium power, does Canada need the capability provided by fighter aircraft as it moves into the twenty-first century? Will Canada require the ability to engage in future hostile aerospace environments? Will Canada need to have airpower to defend its borders and to defend future foreign deployments? These are the real questions that need to be asked. Once these questions have been addressed, then the issue of costs may be examined. Keywords: Canadian AirpowerF-35Military StrategyCanadian Defence Policy Notes Of the many Americans who examine the modern concept of air power, RAND is one of the foremost think tanks examining issues relating to the subject (http://www.rand.org/topics/national-security.html). Within Canada there is now the Canadian Forces Aerospace Warfare Centre. But among non-military research institutes only the Centre for Defence and Security Studies at the University of Manitoba has a focus on Canadian air power. Additional informationNotes on contributorsRob Huebert Rob Huebert is an associate professor in the Department of Political Science and the associate director of the Centre for Military and Strategic Studies at the University of Calgary. He was a senior research fellow of the Canadian International Council; a fellow with Canadian Defence and Foreign Affairs Institute and in November 2010 was appointed as a commissioner to the Canadian Polar Commission. Department of Political Science, University of Calgary, Calgary Alberta, T2N 1N4.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".