Bibliographic record
Abstract
Both of us have been working on different aspects of the peace-and statebuilding intervention in Afghanistan over the past few years, including Canada's role and standing therein.The idea for this project emerged during the discussions in one of our monthly research colloquiums on Peace, Conflict, and Transition.Laura's PhD project assesses the effectiveness of Provincial Reconstruction Teams (PRTs) in Afghanistan.Her case studies include Canada and the US.While the colloquium vividly discussed the Afghan peace-and statebuilding mission and Canada's role therein, we started to realize that there is an apparent, and wide, gap in the literature assessing Canada's development programs and projects in Afghanistan.Since both of our earlier research and writing projects had closely intersected with the development side (known as the security-development nexus in the literature), we decided to explore this project a bit more by probing some of the data that we had already collected through our joint field research in Washington, DC, in June 2017.Although we knew that the data was difficult to get hold of and is extremely complex, we saw sufficient promise to start working on this joint project.However, writing an analysis of Canada's development programming in Afghanistan is by no means an easy task, and we benefited significantly from colleagues in the writing process.In overcoming a number of theoretical and methodological obstacles, we benefited tremendously from the research assistance of Shermeen Umar Khan, Ikram Handulle, and Esengul Tasdemir at the University of Ottawa, who helped us to trace and collect the information on each of the development programs that Canada engaged in in Afghanistan.This was not only a tedious task, it was also sometimes confusing because
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.474 | 0.278 |
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".