I am a scientific writer, independent researcher, and mom of three. My scientific background includes laboratory work in infectious disease and plant breeding and field work in agricultural science.
Currently, I am working on a metascience project inspired by my own and colleagues' experiences in life science research and by my reading in ancient/historical science (Humble Science Project)
PhD Chemical Biology, Harvard University, April 2015.
Dissertation research on malaria transmission at the Harvard School of Public Health (where I first encountered EA ideas)
BS Plant Science, Cornell University, January 2008.
Hi Christian, thanks for your reply! I'd love to talk about some related ideas I've been thinking about. What's the best way to get in touch?
These food ideas definitely have potential, but it seems like field testing would play an important role in improving their practicality and ways to deploy them.
The world is now facing one of the worst food crises in memory, with famine-like conditions in multiple countries, and conditions have worsened significantly over the past few years. If we're not moving toward feeding everyone today, it seems like it would take several miracles for us to be able to feed everyone in a much larger crisis.
https://www.wfp.org/global-hunger-crisis
Is ALLFED working with organizations that have experience with launching innovative nutritional products and launching them in real crisis situations (such as Action Against Hunger)? I realize that ALLFED is mainly focused on research. I'm just remembering my teachers in agricultural science who told me how their plans and what they thought they knew went out the window when they came into contact with real-life situations. And I've experienced the gap between how researchers see their research results and how farmers can see the same results.
A related problem is that because many AI and non-AI algorithms rely on past data to make predictions, these algorithms can reinforce status quo biases. The algorithm doesn't have to be very advanced or "intelligent" for this to be the effect. If the results are trusted inappropriately, the result can be propagation of harmful situations forward in time. Here's an article discussing a healthcare algorithm that reinforced a racial bias in spending and simultaneously caused funds to be spent very inefficiently: https://lnkd.in/e5_kiHhw
https://lnkd.in/eAA2iFbn
A related problem is that researchers who cite other published research sometimes misinterpret that research or take findings out of context, and this can be hard for readers of the new paper to detect. I've learned to be suspicious of meta-analyses for this reason. On numerous occasions in my work (mostly in infectious disease research), I've gone to check underlying references and found that they were either misquoted or missing important context that affects the interpretation.
A five-sentence letter to the editor of the New England Journal of Medicine, which appeared in 1980, was cited hundreds of times during the early years of the opioid crisis in the 1990s, usually to support claims that opioid addiction is very rare when opioids are medically prescribed. This letter may have played a significant role in fueling the crisis. https://www.nejm.org/doi/10.1056/NEJMc1700150 The letter did in fact report on hospitalized patients prescribed opioids, and the authors did find that it was very rare for opioid addiction to develop during the closely monitored hospital stay. However, the study was not peer reviewed, included a single hospital, did not follow the patients to see if they were addicted after they went home, and did not include any data on patients prescribed opioids for use at home.