Bibliographic record
Abstract
Around the world, the current political conjuncture is one of profound challenges for constitutionalism and the rule of law. In the United States, the executive has willfully engaged in a prolonged attempt to weaponize the machinery of the state and radicalize public opinion in order to undermine a democratic election. In the European Union, the increasingly authoritarian relationship between the executive and the judiciary in Poland and Hungary is posing the most profound threat to European constitutionalism in decades. In Hong Kong, the Chinese state is actively seeking to undermine legislative and judicial independence in the face of unprecedented pro-democracy mobilizations. In India, Lebanon, Bolivia, and elsewhere mass mobilizations are challenging, and being suppressed in the name of, the rule of law. Here in Canada, the Wet’suwet’en and their supporters, as well as the Tsleil Waututh, Haudenosaunee, L’nu (Mi’kmaq), Inuit, and members of countless other Indigenous nations are contesting the very nature of the rule of law, as they assert Indigenous laws against the law enforcement of the colonial state. Around the world, the use of emergency powers in response to the COVID-19 pandemic is also raising profound constitutional concerns.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".