I am a Wicked Problem, too: A systemic design strategy for addressing procrastination and anxiety
Bibliographic record
Abstract
It is perhaps easy or natural to think of systemic design at the level of communities, organisations, industries, ecosystems, or governments. As a discipline, after all, systemic design is largely developing in response to the pressing challenges influencing society at these levels. However, Lockton (2018) showed how systemic design also applies to wicked personal problems in a callback to the “knots” derived from the psychiatric practices and poetry of R.D. Laing and the “double-binds” of Bateson. In concluding, Lockton (2018, p. 429) asks: “Could we help people identify knots in their own lives (and help them untangle them?) Is it even possible to untangle these? Do they describe problems that have a wickedness to them which means attempting to untangle creates a whole new problem?” In this presentation, I try to answer this call by applying systemic design methods to a personal problem via an autoethnographic case study. Sparked by the dysfunctionally late realisation that I am a wicked problem, too, I have been making progress on my own procrastination problem with systemic design. I present an analysis of my own attempts to eradicate this disorderly habit by reframing it as a systemic design challenge—and me, and my tools, as a socio-technical system. Here, I share how systemic design modelling has played three roles in this progress: model as a diagnostic tool, model as treatment strategy, and model as therapy. Applying systemic design principles and tools helped me appreciate the complexity of this problem, design strategies for change, and identify novel, creative solutions that gave me leverage over some of the root causes and serve as a day-to-day disentangler when I get stuck in the tangled loops of my own anxieties. Key contributions of this presentation include the framing of personal behavioural change as wicked problems, the demonstrated combination of autoethnography and systemic design modelling, the three roles of modelling in progressing personal, systemic change, and the model of procrastination-driven anxiety and its insights.
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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 | 0.096 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.016 | 0.080 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.006 | 0.013 |
| 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".