Mining the Digital Information Networks
Bibliographic record
Abstract
The main theme of the 17th International Conference on Electronic Publishing (ELPUB) concerns different ways to extract and process data from the vast wealth of digital publishing and how to use and reuse this information in innovative social contexts in a sustainable way. We bring together researchers and practitioners to discuss data mining, digital publishing and social networks along with their implications for scholarly communication, information services, e-learning, e-businesses, the cultural heritage sector, and other areas where electronic publishing is imperative. ELPUB 2013 received 36 paper submissions. The peer review process resulted in the acceptance of 16 papers. From the accepted papers, 8 were submitted as full papers and 8 as extended abstracts. These papers were grouped into sessions based on the following topics: Data Mining and Intelligent Computing, Publishing and Access, and Social Computing and Practices. James MacGregor and Karen Meijer-Kline from the Public Knowledge Project (Simon Fraser University Library, Canada) lead the pre-conference workshop on June 12. The workshop is entitled “The Future of E-publishing: An Introduction to Open Journal Systems & Open Monograph Press”. The main program on June 13–14 features two keynotes. Stephan Shakespeare (YouGov, UK) will deliver a keynote entitled “Getting value out of our digital trace: a strategy for unleashing the economic and justice potential of data sharing”. Professor Felix S. Wu (University of California at DavisUSA) will deliver a keynote entitled “Social computing leveraging online social informatics”. ELPUB 2013 also features a panel discussion entitled “Setting research data free – problems and solutions”. The panel consists of the aforementioned keynote speakers as well as Professor David Rosenthal (Stanford University, USA) and Hans Jörgen Marker (Swedish National Data Service, Sweden). We believe that the topics featured in the program of this year's ELPUB conference are diverse and exciting. Firstly, we would like to thank members of the ELPUB Executive Committee who, together with the Local Advisory Committee, provided valuable advice and assistance during the entire process of the organization. Secondly, we would like to thank our colleagues in the Program Committee who helped in assuring the quality of the conference throughout the peer reviewing process. Lastly, we acknowledge the Local Organization team for making sure that all efforts materialized into a very interesting scientific event. Thank you all for helping us maintain the quality of ELPUB and deserve the trust of our authors and attendees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".