Bibliographic record
Abstract
Abstract : It was an honor for the Joint Special Operations University (JSOU) to host the February 2013 Special Operations Forces (SOF) Symposium, The Role of the Global SOF Network in a Resource Constrained Environment. This symposium represented the second year in which JSOU and the Canadian Special Operations Forces Command (CANSOFCOM) have co-sponsored this event. We built upon the prior symposium hosted by the CANSOFCOM Professional Development Centre held in December 2011 at the Royal Military College of Canada in Kingston, Ontario. In that symposium, we explored the issues and challenges of SOF personnel training, mentoring, and collaborating with SOF from our partner nations around the world. This symposium moved us forward and focused on the future integration and interoperability necessary to sustain the emerging Global SOF Network with the realization that we will have to do this more efficiently and effectively. With anticipated resource constraints in the future, it will require a synchronized and interoperable Global SOF Network to combat our current and emerging threats. A network is inherently human in the SOF realm. Although our operators have a distinct advantage of access to some of the best equipment and systems in the world, it is the understanding and leverage of the human domain that serve to strengthen the bonds of trust among our partners while providing us an advantage in operations against our adversaries. Those bonds among our partners represent commitments that must be nurtured and sustained so we develop, together, both commonality of experience and trust.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".