<i>A History of the Republic of Biafra: Law, Crime, and the Nigerian Civil War</i>. By Samuel Fury Childs Daly
Bibliographic record
Abstract
As I read this book on the Biafran War and its aftermath, the COVID-19 pandemic disrupted rights and mobility everywhere to varying degree, ushering in a state of prolonged uncertainty across the world. This present-day backdrop made it easier to imagine the cumulative and increasingly grave effects of the changes to daily life that war ushered in to 1970s Nigeria following the Republic of Biafra’s brief existence. Daly’s central argument is that “It is impossible to understand Nigeria’s long experience of crime without the context of the Nigerian Civil War—specifically, the survival tactics that Biafrans and Nigerians developed to cope with wartime dangers and postwar hardships” (13). Criminality is not inherent to Nigeria as popular and scholarly portrayals too often suggest. Rather, it is contingent, arising from the war and its aftermath. Using evidence gathered from incomplete court records, memoirs and supplemented with some thirty oral interviews (2, 23-6), Daly...
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".