Bibliographic record
Abstract
The transition from the EndSARS movement to increased harassment by the Economic and Financial Crimes Commission (EFCC) in Nigeria signifies a troubling swift shift in governmental repression. Although the EndSARS protests seemed to have successfully led to the dissolution of the notorious Special Anti-Robbery Squad (SARS), they inadvertently led to a new form of persecution – EFCC. This study employs Discourse Analysis (DA) and this approach is particularly suited for analyzing the discourses around EndSARS and the EFCC, given the power struggles, ideological conflicts, and societal impacts involved. Drawing from sources which include news articles and editorials from major Nigerian newspapers and online news platforms, social media posts, comments, and hashtags related to EndSARS and the EFCC on platforms such as Twitter, Facebook, and Instagram, the study shows that the EFCC, initially established to curb financial crimes, has begun targeting the youths, employing similar tactics of arbitrary arrests, detentions without trial, and abuse of power. This transition from police brutality to financial harassment shows the rising wave of abuse of authority within Nigeria’s law enforcement agencies, undermining public trust and violating both national and international legal rights. Consequently, the frequent detention of individuals, seizure of property, and public shaming of arrested individuals by Nigeria’s EFCC can impede business operations, leading to financial losses and, in some cases, the demise of fledgling enterprises This article explores the consequences of this transition, emphasizing the urgent need for comprehensive reforms to safeguard individual freedoms and uphold the rule of law in Nigeria.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".