Bibliographic record
Abstract
Security in Africa continues to be problematic to both scholars and practitioners. Its study often takes an itemised approach where actors are studied in detail and security outcomes are linked to the effectiveness or ineffectiveness of actors. Perceived and actual security threats are correlated to conflict or presented as causal factors of conflict. In other words, security provision is explained through an itemised and reductionist analysis of security actors. In the past few decades, it is increasingly evident that non-linearity is pervasive in all forms of social organisation. This article rejects the Newtonian paradigm. It is argued that security is often a product of a system, which can be a complex adaptive system (CAS). It contends that a resilient security system guarantees a minimum level of security. To support this argument, empirical evidence from Cameroon is used to prove that Cameroon’s security system is a CAS. The conceptualisation of Cameroon’s security system as a CAS enables the application of both complexity science and resilience perspectives to security analysis. These perspectives allow the argument that Cameroon’s security system is resilient. The characterisation of Cameroon as fragile, failing or failed is rejected.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".