Bibliographic record
Abstract
<JATS1:p>Leading Horn of Africa expert Charlotte Touati exposes the role played by Canadian gold mining company Nevsun and other global actors in propping up the regime of Isayas Aferwerki, one of Africa’s most dangerous dictators.?</JATS1:p> <JATS1:p>In so doing, Touati shows how global capital networkshelp perpetuate economic and political instability in the Horn of Africa, which in turn is fostering violence and volatility throughout other parts of the world.</JATS1:p> <JATS1:p>Using a narrative framework and a core cast of characters to help guide non-specialist readers through her findings, Touati explains the enormous significance of how Nevsun partnered with Aferwerki to open the Bisha gold mine and save his regime from bankruptcy. As Touati relates, when Eritrean refugees later claimed they were “conscripted” to work in the mines without pay and abused as part of their National Service, Nevsun hired lobbyists to defend Eritrea’s actions and cast the very notion of human rights as a Western “Trojan horse.” Australian, Chinese, and Russian actors gradually became involved, and philological analysis shows that the propaganda and disinformation campaigns of the infamous Russian Wagner Group ultimately stem from the rhetoric of Afewerki and his lobbyists.</JATS1:p> <JATS1:p>Ultimately, this violently anti-Western rhetoric was injected into a pan-Africanist discourse to become an ideological and rhetorical toolbox. It was used to prevent any intervention on behalf of Eritreans trapped in their own country or Tigrayans genocided in neighboring Ethiopia in the name of sovereignty, and also to legitimize new conflicts throughout the Sahel and the rest of Africa's notorious “Coup Belt.” It takes on even wider global dimensions when we consider the ongoing crisis around the Red Sea ports, which Eritrea controls, and the question of the influence of the Gulf States in the Horn of Africa.</JATS1:p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".