Bibliographic record
Abstract
In this article, I engage in a brief thought experiment concerning two important yet not often-addressed aspects of constitutional-design theory. First, I place constitutional design in the broader context of what I call the 'design sciences' — the many disciplines, domains, and activities from urban planning to space exploration — that rely on design to accomplish big, noble goals. Second, I address the question of 'success' in constitutional design, namely how to define and assess the actual impact of constitutional structures in accomplishing desirable objectives. Among the possible criteria I examine are time horizon and endurance; actual implementation of constitutional aspirations; constitutional design’s contribution in accomplishing substantive goals such as democracy, prosperity, and human development; the real success rate of constitutions in mitigating existential tensions in conflict or post-conflict settings; and the (in)ability of constitutions to address some of the world’s biggest challenges, from health pandemics or climate change to widening social and economic gaps, forced migration, proliferation of weapons of mass destruction, or persisting large scale crimes against humanity — all of which require global cooperation and therefore lie largely beyond the reach of constitutions.
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 | 0.097 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".