This is a read only archive of pad.okfn.org. See the
shutdown announcement
for details.
unfestival_trackingbadscience
DARK BIBLIOMETRICS
Contacts
- Minuette Le - @hellominh - minuette.le@gmail.com
- David Stark - @zarkonnen_com - zarkonnen@gmail.com
- Fabrizio Scrollini @fscrollini fabrizio@datauy.org
- Tomas Apodaca @tmaybe tomas.apodaca@asiafoundation.org
Key issues
- Fraud: Examples from Japan of faking 40 papers
- Editorial "Gangs" You only get published if you are "in"
- Fake journals
- Predatory Journals
- Very dark ways of measuring this and people not understanding it
- Feedback loop from academics not being used E.g. I read a paper ...and what do i do? tweet about it?
- Libel?
Normal metrics: positive incentives. Dark metrics: -ve incentives.
Core idea
Network of papers, contaminating authors, papers, editors, publications.
Concerns
- Rating papers?
- Different errors: mistakes, fraud, bad data, bad analysis.
- Quality = 1 / badness.
- Is this libel?
- Jeffrey Bealle - a kind of watchdog with a blacklist
- Some citations are positive and some negative
- Has someone done NLP analysis on +ve/-ve citations?
- Probably some fields easier to start with: quantiative domains, experimental domains (proving or disproving issues)
- Other groups collecting metadata?
- Alt-metrics
- Isolated clusters of people - a bad sign or just something that happens?
- Content Mine
Next Steps
- Contact other projects.
- Find an angry medical student as a specialist / spokesperson.
- Figure out if +ve/-ve citations can be disambiguated.