4 ms·

That supervisor is someone who "made it in academia" so it might be good to not sneer and cringe at them and your peers.

by anjc 8y ago

fock 8y ago
I know. But basically at this point I'd say that a large portion of the academic output is crap even in "hard" science. Also when looking at experiments (did that too in a lab course, where I got data from an existing apparatus): "Interpolating" data with a spline. "No, your result is not good, I get different result". "Maybe you should get more data then". "No, data's good, we need different result, also colormap is bad, use same like Matlab". "Well, the matlab colormap is colorful but not true to reality". "No, I see interesting things in plot with Matlab colormap". Stopped arguing, went back home, used jet instead of viridis and smoothed the spline. Got an A+. What a great day! And while I might be not the brightest guy around and thus might not be able to just run around spitting out solutions for hard problems I have certain standards on integrity. Basically faking results is something I won't do to create (optional, published) work (which a paper is for undergrad work and also was for a PhD until not too long ago here...).
- anjc 8y ago
  Luckily the academic system will typically weed out unethical behaviour (e.g. faking results).
  - fock 8y ago
    If you think the current process does, you either are in a very good environment or are plainly lying to yourself. I could just add some bits here and there, get excellent results and publish. Probably nobody will notice as it won't be a "big issue" and seems all fine with existing experimental data.
    - anjc 8y ago
      If you've faked your data to get these results then it could mean the end of your career, hence must be a very uncommon practice. This is a different from cherrypicking data, or putting a positive spin on your findings. But again, it's expected now that data and code are open, so you wont get away with much these days in high-impact conferences/journals.
      - fock 8y ago
        Not talking about high impact journals (where the majority of papers does not end up, when everyone is expected to publish twice a year...). Also I think cherrypicking your ML training data to achieve a certain result is akin to faking results?! And if you think that all data and code are open today you should certainly take a better look around you. Half of our lab is running on proprietary (simulation) software, good luck with auditing the code there (and evaluating the data because most of the people don't really document there workflow...). And both these examples take/took place on upper-mid-tiere european universities which consistently rank in the global upper 100/european upper 20-citation rankings... Besides that, most of the people do a PhD for getting better paid jobs, not academic tenure (where such things could backfire).
    - deleted 8y ago
      [deleted]

That supervisor is someone who "made it in academia" so it might be good to not sneer and cringe at them and your peers.

by anjc 8y ago

That supervisor is someone who "made it in academia" so it might be good to not sneer and cringe at them and your peers.

fock 8y ago

I know. But basically at this point I'd say that a large portion of the academic output is crap even in "hard" science. Also when looking at experiments (did that too in a lab course, where I got data from an existing apparatus): "Interpolating" data with a spline. "No, your result is not good, I get different result". "Maybe you should get more data then". "No, data's good, we need different result, also colormap is bad, use same like Matlab". "Well, the matlab colormap is colorful but not true to reality". "No, I see interesting things in plot with Matlab colormap". Stopped arguing, went back home, used jet instead of viridis and smoothed the spline. Got an A+. What a great day! And while I might be not the brightest guy around and thus might not be able to just run around spitting out solutions for hard problems I have certain standards on integrity. Basically faking results is something I won't do to create (optional, published) work (which a paper is for undergrad work and also was for a PhD until not too long ago here...).

anjc 8y ago
Luckily the academic system will typically weed out unethical behaviour (e.g. faking results).
- fock 8y ago
  If you think the current process does, you either are in a very good environment or are plainly lying to yourself. I could just add some bits here and there, get excellent results and publish. Probably nobody will notice as it won't be a "big issue" and seems all fine with existing experimental data.
  - anjc 8y ago
    If you've faked your data to get these results then it could mean the end of your career, hence must be a very uncommon practice. This is a different from cherrypicking data, or putting a positive spin on your findings. But again, it's expected now that data and code are open, so you wont get away with much these days in high-impact conferences/journals.
    - fock 8y ago
      Not talking about high impact journals (where the majority of papers does not end up, when everyone is expected to publish twice a year...). Also I think cherrypicking your ML training data to achieve a certain result is akin to faking results?! And if you think that all data and code are open today you should certainly take a better look around you. Half of our lab is running on proprietary (simulation) software, good luck with auditing the code there (and evaluating the data because most of the people don't really document there workflow...). And both these examples take/took place on upper-mid-tiere european universities which consistently rank in the global upper 100/european upper 20-citation rankings... Besides that, most of the people do a PhD for getting better paid jobs, not academic tenure (where such things could backfire).
  - deleted 8y ago
    [deleted]