Don’t forget about Boxer: Teaching Systems Thinking, Complexity and Design to NCMs
Bibliographic record
Abstract
Since 2009, Robert has worked with the team at the Osside school at RM Saint-Jean, working on all courses. Prior to this, Robert completed an internship at UNICEF HQ, working with the Emergency Unit team which staffed experts for emergency situations. He also worked as a researcher for the Presidential Human Rights Commission of Guatemala, through the Canadian International Development Agency young professional program. With National Defence, Robert has participated as an instructor in military training and cooperation missions (DMTC) in Jordan, Brazil and Philippines.Additionally, Robert has participated in electoral observation missions, such as Mission Canada 2012 and 2014 in Ukraine, for the Organization of American States (OAS) in Guatemala 2011 and for an NGO in El Salvador 2009.Robert is interested in the interconnections between diverse subjects: development, conflict, the global economy, complex systems, cultural intelligence, ethics, the Women, Peace and Security agenda and post-conflict contexts. He has traveled to about 30 countries. His education (M.A. McMaster, 2007) is in Political Science – international relations and comparative politics of developing regions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".