Praise for New York Times biz reporter

STATS, a blog at George Mason University that examines the usage of statistics and science in the media, praises New York Times business reporter Melanie Warner in a Monday posting.

Trevor Butterworth, a contributing editor to the site, writes that Warner “is doing an astonishingly brilliant job of covering the nexus between science, health and business from the pages of the Times Sunday Business Section. Her latest article, A Sweetener with a Bad Rap, is a superbly reported analysis of whether high fructose corn syrup should be seen as a culprit for the increasing rates of obesity in America. The problem isn’t just that there seems to be a remarkable paucity of hard evidence for the idea that there’s something specific to fructose that’s more fattening than regular sugar,

“‘Scientists say part of the confusion about the ingredient’s role in the nutrition debate stems from a basic misunderstanding: the idea that high-fructose corn syrup is actually high in fructose.’

“Unlike some rivals who map similar terrain, Warner is not in thrall to the activists on the health and business beat, and she doesn’t let their soapbox agendas dictate the narrative pitch. This may seem like an obvious journalistic standard to adhere to, but many reporters have a curious phobia about including independent scientists and experts in their coverage of health risks (The Los Angeles Times and Wall Street Journal on phthalates in cosmetics for example) – or giving industry sources or government regulators a fair chance to make their respective cases (most recent coverage of the supposed risks from Teflon – and the Times’ mammoth yet staggeringly incomplete investigation of the risks of accidental addiction from OxyContin).

“Warner avoids the quick scare-mongering thrill for genuine analysis – and the results make for compelling reading. (We were particularly impressed by her reporting on the risks from aspartame back in February). This makes her an appointment reporter: someone who can be relied on to see shades of gray when the data is murky and yet call out black and white when the data is clear.”

Read more here.