Bibliographic record
Abstract
Constitutional preambles grow ever longer, more complex, and more present in public debate. Extant theories note their descriptive or symbolic roles, but leave key elements, such as the use of historical recitation, untouched. A core purpose of such elements is legitimation. Because constitutions are not just legal documents but when promulgated, contentious events, leaders must sell a constitution to a sometimes sceptical or fractured citizenry. To sell the constitutional future, preambles cite the past. While the substance of past events matters, the arc of time traced out by joining the dots between events, also does rhetorical work. These narrative arcs have familiar shapes: progressive, cyclical, or eschatological. We recognize this type of story, and we know what type of thing happens next. By situating the new constitution as an event along such a recognizable arc of time, citizens can infer a hopeful future from the shape of a strategically constructed past. While not all historical preambles use “temporal framing” as a rhetorical strategy, the technique is common, and, here, illustrated through in-depth engagements with China’s and Hungary’s constitutional preambles.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".