Bibliographic record
Abstract
The academic study of federalism is somewhat unfashionable in Africa, where formal institutions are often regarded as superficial, ephemeral and ineffective, while informal norms, networks, processes and practices are considered to be the real bedrock and substance of politics. Indeed, for decades, a “neo-patrimonial theoretical framework” or “institution-less school” has been the prevailing paradigm for analyzing African governance and politics (Cheeseman 2018, 10-12). As a concept, neo-patrimonialism focuses on the pathologies of personal, “big man” rule, corruption, predation, patron-client networks and other informal ruling mechanisms in Africa. African structures of personalist rule and relations, in this neo-patrimonial conceptual framework, have little or no place for formal federalist institutions of self-rule, shared rule, and limited rule. Consequently, federalism is often regarded as irrelevant, unviable, or invariably doomed to degradation, extinction, and administrative, fiscal, and political recentralization in Africa’s neo-patrimonial governance eco-system. “In short,” in the words of a leading scholar of decentralization in Africa, “federalism can hardly matter where [formal] institutions themselves have little import” (Dickovick 2012, 3).
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".