Bibliographic record
Abstract
Under asymmetric federalism, the constituent states of a federation have different powers and responsibilities. In many well-known cases – including India, Canada, and Russia – asymmetric arrangements are introduced to address the demands, concerns, or secessionist impulses of specific ethnic, cultural, linguistic, or religious groups. They are sometimes set out formally in the national constitution, but may also be less formal, consisting, for example, of special arrangements and opt-outs negotiated between national and subnational governments. Asymmetric arrangements may also be introduced in unitary rather than federal systems. South Africa is a unitary state and its system of provincial government – which partially reflects racial, ethnic, and historical legacies – is essentially symmetric, though there are major divergences in economic development, human development, and state capacity across the provinces. In at least two cases – Gauteng and the Western Cape – there is a strong case to be made that provinces should take on competencies that are currently located at the national level. But if this devolution were to occur, it would have to occur asymmetrically, given the weakness of other provincial governments. That prospect raises a number of potential problems, as well as potential opportunities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".