Enhancing Challenge Framing in Defence Organisations: Towards Reflexive Methods
Bibliographic record
Abstract
This article contributes to problem solving, design, and planning in defence organisations by arguing that a ‘problem’ or a ‘challenge’ is never objective, natural or ready-made. Challenges are contingent to the conditions under which individuals perceive and formulate them. As a result, this article understands ‘challenges’ and ‘ap- proaches’ to address them as co-dependent on one another. This article recommends that officers should attempt to generate the most interesting and, we hope, innovative problem-solution pair or challenge-approach pair in order to integrate this insight into practice when problem solving, designing, or planning. Leaders and their teams can learn to inhabit this mind-set by finding inspiration in three modes observed through practice: initial challenge framing, challenge curation and co-evolution. For each of these modes, the article proposes reflexive methods and tools for enhancing introspection in challenge framing and formulation namely the Five Whys, question-storming, and loyal opposition. The article supports these recommendations and methods through insights gleaned from philosophy of knowledge, design theory, and on design experiences with the North American Aerospace Defence Command (NORAD) in 2019.
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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 | 0.164 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".