A Proposed Analytical Framework for Canadian Whole-of-Government Lessons Learned
Bibliographic record
Abstract
Meta-organizational approaches involving multiple government agencies or levels of government, military-civilian combinations, multi-sector or international coalitions are becoming increasingly standard practice for addressing complex operations. One of the challenges in the post-event lessons learned period has been the lack of an adequate analytical framework, which has led to the re-identification of key issues in similar lessons learned processes and documents each time. The objective of this chapter is to build upon the existing knowledge base of identified lessons and reduce the learning curve for future analysts. By developing a framework for the collection and analysis of whole-of-government lessons learned, future practitioners will have a consistent set of parameters upon which to develop their core collection plans, a structure for analysis, and be able to identify known risks to mission success. Drawing upon Canadian and international experiences from whole-of-government and comprehensive approaches, the chapter provides two considerations for issues analysis: a derived critical topics list for meta-organizational approaches and a capabilities framework that could be applied to lessons learned approaches in these kinds of complex initiatives.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".