The SAS-027 Historical SSC Database with Application to an Analysis of Past Canadian Operations
Bibliographic record
Abstract
NATO's Studies Analysis and Simulation (SAS) Panel has sponsored a series of studies on long term defense planning (LTDP) issues in recent years. Study SAS-027 was given the mandate to investigate methods and techniques suitable for supporting LTDP with the focus on Smaller Scale Contingency (SSC) operations. The term 'SSC' is widely defined to include any operation short of war this includes the full range of peace support operations and humanitarian and disaster relief efforts. Many NATO and 'Partners for Peace' nations participated in the study as well as other invited nations such as Australia. Nations have been conducting SSC operations for decades. Indeed Canada has established its international reputation as peacekeepers. However the end of the Cold War has permitted NATO nations to view participation in SSC type operations as more of a primary rather than secondary role resulting in increased frequency of participation in such operations. Canada like most NATO nations during the Cold War designed its force structures on the notion that if one prepares for mid-intensity conflict in Europe against the Warsaw Pact threat then one should be able to handle the less demanding spectrum of SSCs. Although there is some logic to this reasoning in terms of operational effectiveness we are discovering that defense forces designed for a Cold War role are not necessarily fully effective nor in the right balance to handle the number and diversity of SSC operations faced today. The SAS-027 study covers the full range of issues surrounding LTDP for SSCs focusing mainly on analytical methods supporting data sources and a code of best practice for applying these methods and data. One of the fundamental data inputs to any LTDP exercise will be historical information on SSC operations that have conducted in the recent past. This paper outlines the historical data collection exercise that was led by Canada.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".