This is a second signal-detection analysis of the accuracy
of a robot in detecting open access (OA) articles (by checking by hand how many of the articles the robot tagged OA were really OA, and vice versa). We found that the robot significantly overcodes for OA.
In our Biology sample, 40% of identified OA was in fact OA. In our Sociology sample, only 18% of identified OA was in fact OA. Missed OA was lower: 12% in Biology and 14% in Sociology.
The sources of the error are impossible
to determine from the present data, since the algorithm
did not capture URL's for documents identified as OA.
In conclusion, the robot is not yet performing at a desirable level, and future work may be needed to determine the causes, and improve the algorithm.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105417 |
| Date | 12 1900 |
| Creators | Antelman, Kristin, Bakkalbasi, Nisa, Goodman, David, Hajjem, Chawki, Harnad, Stevan |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Technical Report |
Page generated in 0.0016 seconds