Bibliographic record
Abstract
Criminal victimization against agent bankers and their customers has recently been on the increase, and the multiplier effect is a major socio-economic problem that could cause a downturn in the economy. This study examines the exposure of agent bankers and their customers to criminal victimization in Ado city, Nigeria. Specifically, the study explores the forms of criminal victimization against agent bankers and their customers, examines the consequences of criminal victimization, looks into the cases of agent bankers and customer victimization, and determines the preventive measures adopted by agent bankers and their customers against victimization. Lifestyle-routine activity theory was deployed as the conceptual framework. An exploratory research design and snowball and purposive sampling with key informant and in-depth interviews were used to interview 12 victimized and non-victimized agent bankers and their customers. Interviews were conducted in Fayose market, King market, Bisi market, and Irona market. Findings reveal that agents’ bankers and their customers were being targeted by criminals who use non-violent tactics. The eagerness of the agent bankers to transact business brings a measure of carelessness to their business dealings. This carelessness and lack of security measures exposes them to motivated offenders. Victimization through fake alert, fraudulent transfer, withdrawal under false pretense, fraudulent alteration, intentional criminal patronage, fake identity, and urgent withdrawal under duress represent the themes found. It is imperative for agent bankers and their customers to set up security measures that could protect them against being victimized.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".