If your landlord changes the locks on your apartment because you owe two months rent, don't break the door down and don't fight him at the gate.
I know the anger, but that single moment of "self help" can turn you from the wronged party into the accused.
Under Nigerian law, a landlord cannot just throw your things outside or lock you out without going through the proper court process, no matter how much rent you owe. What he did is illegal self-help, and there's a difference between owing rent and losing your right to be removed from a property without due process.
That moment is your evidence, not your fight. Take photos and videos of the locked door, get a witness if you can and do not attempt to force entry.
That is when you call your lawyer, because this is now a matter for a magistrate court or the appropriate tenancy tribunal, not for fists.
If you don't have a lawyer already, that's what @UseCaseRadar is for. Sign up at https://t.co/oiXOD1bKFW or download the Case Radar app and connect with a lawyer wherever you are, day or night.
@WayneRooney@ColeenRoo@WayneRooney would ya consider yourself a striker or a Mildfielder in your days?? Even though you played a little bit of both. You can only be one anyway. But which one??
“If you haven't read hundreds of books, you are functionally illiterate, and you will be incompetent, because your personal experiences alone aren't broad enough to sustain you.”
— James N. Mattis
Turnitin has become a lucrative business by selling the promise of certainty in an age of AI uncertainty.
But the evidence keeps reminding us that AI detection is not as straightforward as many institutions would like to believe.
I recently read Lucky E. Atamhenwan’s 2026 paper, How are combinations of human-written words and LLM-generated words by ChatGPT, Copilot, Gemini and Grammarly detected by Turnitin?, and the findings are important for anyone involved in teaching, assessment, or academic integrity.
The study tested 81 scripts with different combinations of human-written and LLM-generated text, ranging from 100% human-written to 100% AI-generated. The AI-generated text came from ChatGPT, Copilot, Gemini, and Grammarly.
One of the key findings is that Turnitin did not flag fully human-written texts. It also did not produce AI scores when only 5% or 10% of the text was AI-generated.
But once AI-generated content reached around 15%, Turnitin began producing AI scores. The problem is that those percentages were often inaccurate.
At lower levels of AI-generated text, Turnitin tended to overestimate AI use.
At higher levels of AI-generated text, Turnitin tended to underestimate AI use.
Even more striking, detection varied depending on the tool used. ChatGPT-generated text, for instance, was often underdetected. In one case, a script that was 100% ChatGPT-generated received a Turnitin AI score of only 60%.
The study also showed that some “humanizing” or paraphrasing tools can significantly reduce detection. In some cases, Turnitin returned 0% AI scores for texts that were originally 100% LLM-generated and then humanized.
AI detectors may provide a signal, but they should never be treated as proof. A Turnitin AI score is not a verdict and should never be a confession!
I think instead of asking: “Was AI used?”, we better ask: “How was AI used, why was it used, and does that use align with the learning goals and policy expectations?”
Can AI really detect AI?
Currently, universities, journals, and various educational institutions are using AI detectors to identify AI-generated writing.
However, a recently published study has challenged this widely held assumption.
In a study titled "AI Detecting AI in Academic Writing: Why Most AI Detector Findings Are False," published in Elsevier's Elsevier journal Next Research, researchers argue that most results produced by current AI detectors are not reliable and can often lead to incorrect conclusions.
The reason is that modern Large Language Models (LLMs), such as ChatGPT, have become so advanced that even experts often find it difficult to accurately distinguish between human-written and AI-generated text.
Nevertheless, many institutions are treating AI detector reports as if they were definitive evidence.
The study also shows that AI detectors frequently misclassify human-written content as AI-generated.
One of the study's most important findings is that when the actual prevalence of AI-generated writing is low, the false-positive rate of AI detectors increases dramatically.
In other words, if AI use is relatively limited in practice, an innocent author may face a much higher risk of being wrongly accused of using AI.
The researchers further note that authors who do use AI can often evade detection simply by modifying, editing, or rewriting portions of the generated text.
This means that a writer who never used AI may still be accused of doing so, while someone who did use AI may not be detected at all.
According to the researchers, AI detector results should not be used as the sole or definitive evidence of AI usage.