The phrase "false meaning" is grammatically correct and usable in written English.
You could use it to describe when someone has misunderstood the meaning of something, such as, "The student was trying to interpret the poem, but he took away a false meaning."
But reading cast off the accumulated layers of false meaning around the words, and gave language back to me.
In English, the word totem was introduced in 1791 by a British merchant and translator who gave it a false meaning in the belief that it designated the guardian spirit of an individual, who appeared in the form of an animal an idea that the Ojibwa clans did indeed portray by their wearing of animal skins.
Otherwise, isJobMapped is set to false, meaning job j cannot be scheduled to meet its deadline (line 30).
"If the disgusting and corrupt media covered me honestly and didn't put false meaning into the words I say, I would be beating Hillary by 20%," he wrote on Twitter Sunday.
For a time this month, his own campaign took the unusual step of blasting out negative stories about Trump in news releases titled "Media Bias Offender". "If the disgusting and corrupt media covered me honestly and didn't put false meaning into the words I say, I would be beating Hillary by 20%," Trump tweeted this week.
Is it possible to look at a body of data in a way that is technically "true" -- meaning the data is not overtly falsified, but actually "false," meaning the processing of data, because beyond the capabilities of human perception, can appear to be without social, political and economic motivation?
Dr. Heymann of the W.H.O. said some of the early SARS tests produced "many false negatives," meaning that they missed the virus.
In my case, I calculated, even if the rapid test were a false negative (meaning the patient actually had the virus but the test didn't yet show it), the patient's viral load couldn't have been very high.
However, the lean startup method might be producing "false negatives," meaning good ideas are mistakenly rejected because the approach does not have a clear rule for when entrepreneurs and intrapreneurs should declare victory, stop testing, and begin scaling production.
When reading the scans with the help of MRNet, they diagnosed fewer false positives (meaning they were less likely to say a patient had a serious knee injury, when they in fact were healthy) compared to their performance without the algorithm.
As for the decision to stop recommending clinical breast exams, the society said that there was no evidence that the exams save lives, but that there was evidence that they could cause false positives — meaning they could mistakenly suggest problems where none existed and lead to more tests.
