Efficacy of Closed Circuit Television in Security Management of Commercial Properties in Lagos Metropolis, Nigeria
Bibliographic record
Abstract
This study investigates the efficacy of CCTV as a crime control and management apparatus in commercial premises.Shopping malls and storage facilities with closedcircuit television systems as part of security arrangements were identified at different locations across the state.A total of 171 copies of structured questionnaires were administered, and a response rate of 77.2 percent was achieved.Data on safety and security breaches, as well as CCTV footage between 2018 and 2023, were obtained.Descriptive and inferential statistics from Excel analytics tool packs were used for the analysis.Results were presented in tables and figures, followed by a robust discussion.The study observed that CCTV has not been able to completely deter the incidence of criminal acts and that CCTV was found to be more effective in shopping malls than the warehouses, although the difference is statistically insignificant.The study further showed that power and technical issues are the top two challenges of CCTV usage in shopping malls, while personnel and power are the top two challenges at the warehouse.The study suggested an alternative power supply for the CCTV and the use of experts for acquisition, installation, and operation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".