Submission + - Is Scientific Journalism Doomed? (wordpress.com)
scida writes: "I have spent the better half of the past six months trying to understand one thing: how can you effectively present primary scientific literature to the general public? Is this even possible?
There are many facets of Scientific Journalism, but I am only concerned with one here. First, I am not focusing on the coverage scientific work in the development government policy, biographical coverage on individual scientists, or other "newsy" work. I am strictly concerned with the communication and education of the general public of primary scientific information (i.e. what scientists know and publish in their respective academic forums).
I recently attended an interesting seminar, titled, "The Informed Science Journalist: How Much Science Do You Need to Know?" led by UBC journalism Professor and Director of the School of Journalism, Stephen Ward. During the discussion, one theme in particular caught my attention: you don't have to have any background in science to write about science. Anyone with a keen interest for a field and sharp mind can write about anything, from philosophy to advanced string theory to climate modeling.
Is this true? Is a keen interest sufficient?
During the past few months, I have spent entire days locked up in my office, writing my first manuscript to be submitted to a peer reviewed scientific journal. While doing so, I have come to realize the following: details can change everything. There are a number of assumptions I have been forced to make while analyzing my data, many of which are critical for both my methodology and the development of few of my arguments. Why? Often, the information I require simply isn't available (the studies haven't been done, or the studies that exist are based on assumptions of their own).
Now, can someone unfamiliar with a particular field, nay, a sub-discipline of that field, recognize these assumptions for what they are? I can trace the lineage and development of a number of critical assumptions through my sub-discipline's literature that have proven to be incorrect. Ultimately, the focus of the entire field was reshaped, and its direction changed forever as a result of a few "estimations" and assumptions.
Similarly, last year I was involved in organizing a student directed seminar concerned with covering the seminal work of my field over the past 30 years. Three of us canvassed resident professors, professional researchers, and professors and grad students across the world (literally) asking them for their top 20 articles.
I was blown away: more than half of these papers had become nearly obsolete (nearly obsolete, simply because their work was in of itself worthy of admiration for its brilliance). Why? You guessed it — a few key assumptions proven to be incorrect.
How do you explain to someone the relative magnitude of these assumptions? I've often caught myself saying, "Well, 10% error is nothing to be worried about. It's the real world, things aren't that simple." Surely 10% isn't much, but what about 50%? 10 fold? I've come across all of these, and justified every one to my colleagues, all whom agreed with me.
Why? There exists a certain type of intuition associated with information — when you become very familiar with a topic, some things feel more or less "right". I have a 'feeling' what is more or less likely to hold up to scrutiny, just as I can usually tell if someone is trying to pull my lab coat over my eyes.
How, then, do you effectively cover a story laden with valid assumptions, some likely to be correct, many likely to be incorrect? Let us use climate models as an example. In order to avoid long computing times, the use of super computers, or simply (and usually) because the information does not exist, modelers are forced to typically make 100's of assumptions when devising their code. Now, I'm not saying these models are not at all useful. Smart modelers have determined ways of lining up their assumptions with observations of the real world (often, modelers must predict what we already know to verify their assumptions — i.e. does it work?).
Here, the same problem exists — how do you, the science journalist, determine which of these assumptions could bring the entire model crashing down? Furthermore, if such an linchpin exists, is it an important one? How important? Is it likely to be incorrect? How likely? Unfortunately, these questions have no definitive answers, except with respect to each other, and with respect to the particular researcher.
Thus, it appears only the 'scientist' can effectively explain the scope of their work to the general public, assuming they have that ability. The socially inept individuals aside, could the front-line scientists replace science journalists, since they are the most familiar with their own assumptions (and thus the likelihood they are wrong)?
I think the answer is fairly obvious — no. Scientists are humans, and humans have emotions (not all scientists put Spock up on his fairly deserved pedestal). Therefore, this is the same as asking a politician to tell his electorate how his motivation for running for office isn't a personal one. Following that argument, competing scientists could not cover their colleagues work either, for friendships or grudges might get in the way.
Who's left? Everyone on the fringe — those in other fields with a solid understanding in your own, without any of the personal relationships (previous supervisors/bosses/friends/foes/etc) to bias their opinion (there is always bias, but the point is to minimize it).
Does such a network exist? I do not think so. However, it is the only viable solution to a problem that will only get worse as time goes on, andthe leading and developing scientific theories further creep into our everyday lives — a international group of scientists dedicated to the self promotion of their trade via the coverage of their distant colleagues work. The only question is, would anyone scientists step up to such a cause?
Ultimately, I think the majority of the public doesn't truly understand what 'scientific theory' means — either they are overly suspicious of anything scientific, or overly accepting of the 'word of the white lab coats'. In either case, scientific journalists only add to this confusion when sensationalizing recently published work, only to be discredited (the scientists, not the journalists) when something new comes along.
I'm not a professional journalist, but I am a scientist. So, whether any of this was insightful — let me know. If any of it is ludicrous, throw a comment my way. If you have suggested readings, I will give you a giant hug."
There are many facets of Scientific Journalism, but I am only concerned with one here. First, I am not focusing on the coverage scientific work in the development government policy, biographical coverage on individual scientists, or other "newsy" work. I am strictly concerned with the communication and education of the general public of primary scientific information (i.e. what scientists know and publish in their respective academic forums).
I recently attended an interesting seminar, titled, "The Informed Science Journalist: How Much Science Do You Need to Know?" led by UBC journalism Professor and Director of the School of Journalism, Stephen Ward. During the discussion, one theme in particular caught my attention: you don't have to have any background in science to write about science. Anyone with a keen interest for a field and sharp mind can write about anything, from philosophy to advanced string theory to climate modeling.
Is this true? Is a keen interest sufficient?
During the past few months, I have spent entire days locked up in my office, writing my first manuscript to be submitted to a peer reviewed scientific journal. While doing so, I have come to realize the following: details can change everything. There are a number of assumptions I have been forced to make while analyzing my data, many of which are critical for both my methodology and the development of few of my arguments. Why? Often, the information I require simply isn't available (the studies haven't been done, or the studies that exist are based on assumptions of their own).
Now, can someone unfamiliar with a particular field, nay, a sub-discipline of that field, recognize these assumptions for what they are? I can trace the lineage and development of a number of critical assumptions through my sub-discipline's literature that have proven to be incorrect. Ultimately, the focus of the entire field was reshaped, and its direction changed forever as a result of a few "estimations" and assumptions.
Similarly, last year I was involved in organizing a student directed seminar concerned with covering the seminal work of my field over the past 30 years. Three of us canvassed resident professors, professional researchers, and professors and grad students across the world (literally) asking them for their top 20 articles.
I was blown away: more than half of these papers had become nearly obsolete (nearly obsolete, simply because their work was in of itself worthy of admiration for its brilliance). Why? You guessed it — a few key assumptions proven to be incorrect.
How do you explain to someone the relative magnitude of these assumptions? I've often caught myself saying, "Well, 10% error is nothing to be worried about. It's the real world, things aren't that simple." Surely 10% isn't much, but what about 50%? 10 fold? I've come across all of these, and justified every one to my colleagues, all whom agreed with me.
Why? There exists a certain type of intuition associated with information — when you become very familiar with a topic, some things feel more or less "right". I have a 'feeling' what is more or less likely to hold up to scrutiny, just as I can usually tell if someone is trying to pull my lab coat over my eyes.
How, then, do you effectively cover a story laden with valid assumptions, some likely to be correct, many likely to be incorrect? Let us use climate models as an example. In order to avoid long computing times, the use of super computers, or simply (and usually) because the information does not exist, modelers are forced to typically make 100's of assumptions when devising their code. Now, I'm not saying these models are not at all useful. Smart modelers have determined ways of lining up their assumptions with observations of the real world (often, modelers must predict what we already know to verify their assumptions — i.e. does it work?).
Here, the same problem exists — how do you, the science journalist, determine which of these assumptions could bring the entire model crashing down? Furthermore, if such an linchpin exists, is it an important one? How important? Is it likely to be incorrect? How likely? Unfortunately, these questions have no definitive answers, except with respect to each other, and with respect to the particular researcher.
Thus, it appears only the 'scientist' can effectively explain the scope of their work to the general public, assuming they have that ability. The socially inept individuals aside, could the front-line scientists replace science journalists, since they are the most familiar with their own assumptions (and thus the likelihood they are wrong)?
I think the answer is fairly obvious — no. Scientists are humans, and humans have emotions (not all scientists put Spock up on his fairly deserved pedestal). Therefore, this is the same as asking a politician to tell his electorate how his motivation for running for office isn't a personal one. Following that argument, competing scientists could not cover their colleagues work either, for friendships or grudges might get in the way.
Who's left? Everyone on the fringe — those in other fields with a solid understanding in your own, without any of the personal relationships (previous supervisors/bosses/friends/foes/etc) to bias their opinion (there is always bias, but the point is to minimize it).
Does such a network exist? I do not think so. However, it is the only viable solution to a problem that will only get worse as time goes on, andthe leading and developing scientific theories further creep into our everyday lives — a international group of scientists dedicated to the self promotion of their trade via the coverage of their distant colleagues work. The only question is, would anyone scientists step up to such a cause?
Ultimately, I think the majority of the public doesn't truly understand what 'scientific theory' means — either they are overly suspicious of anything scientific, or overly accepting of the 'word of the white lab coats'. In either case, scientific journalists only add to this confusion when sensationalizing recently published work, only to be discredited (the scientists, not the journalists) when something new comes along.
I'm not a professional journalist, but I am a scientist. So, whether any of this was insightful — let me know. If any of it is ludicrous, throw a comment my way. If you have suggested readings, I will give you a giant hug."
