The civil servant systemic designer: An emerging context for systemic design practice
Bibliographic record
Abstract
Introduction. Government decisions manifest within the landscape and can greatly affect change within their jurisdiction and beyond. A perfect example would be decisions about regional energy policy. A government’s views about the production, transportation and consumption of energy within their geography notably impacts land development, resource extraction, economic investment, urban design, transportation, climate change, economic competitiveness and the social mix of a region . Energy policy decisions are foundational to complex predicaments, including energy insecurity, poverty, food and water security and social strife. Notable examples abound but this complexity manifests at the human scale towards whole systems and the spaces in-between.Until relatively recently, government policy development, insofar as it was systemic, relied upon hard systems methodology which began with a knowable problem and converged on a solution . This linear and monistic approach brought depth but lacked context of the wider societal, technological, economic, ecological and political system. In simpler times, and in the absence of complex systems methodologies, this approach was the best option for policy development. Consistent with this view, governments organized themselves around discrete policy silos, each bringing an expert depth to their thematic responsibility. This is opportune where increasing specialization leads to new knowledge, but challenged where context is needed to avoid unintended consequences . Complexity as it is now, calls upon government to navigate policy predicaments with a new architecture – one that brings both depth and context for rigorous policy. \nThis paper examines the emerging context of the civil servant – one entrusted with the public interest by duty and responsibility – who practices within the architecture of government, deploying systemic design methodologies towards the complex predicaments that societies faces. Governments are responding to complexity in policy decisions – design consultants are retained; government staff are trained in designerly ways; and, experienced-designers are employed on in-house consulting teams. These models have the effect of increasing the profile of design practice within government. The model of the Department of Energy in Alberta, Canada is examined with a view towards articulating this emerging context for systemic design practice. The case of a trans-ministry design team applying systemic design methodology around a shared strategic concern is presented
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.011 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".