Design in Civvies: The Promise of Creating Degrees of Freedom in Government
Bibliographic record
Abstract
Both military and civilian design can be measured by the degrees of freedom it unlocks. This paper is a reflection on my experience of civilian and military design reinterpreted through the perspective of unlocking new degrees of freedom. There are many similarities between military and civilian design. The main difference is that it is much more difficult and much less common to create a whole systems team in the military context due to the extreme polarization caused by war. Degrees of freedom can also be created by disrupting the existing system from within or from outside. Disruptive design is ethical only when it alleviates more harm and suffering than it creates.Design is dangerous because it opens up all of our existing structures, institutions and routines to the possibility of redesign. This creates great potential for improvement but also for harm. A responsible designer approaches complex problematic situations with a mixture of courage and humility. They have the courage to act to learn about and to transform undesirable situations, while remaining open to the possibility that their interventions are making things worse. A responsible designer is both an explorer of new territory and a steward of the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".