Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Bibliographic record
Abstract
Welcome to JCDL 2007! It is our great pleasure to welcome you to the 7th annual meeting of the ACM/IEEE Joint Conference on Digital Libraries (JCDL). JCDL is one of the primary international forums for the presentation and discussion of research, practice and social issues related to digital libraries. The conference theme this year is Building and Sustaining the Digital Environment and the program reflects these themes as well as the broader context of digital libraries and the boundary spanning research that supports their design, development and operation. This year we had a record number of submissions for the conference with 279 total submissions from digital library researchers in 31 countries. From 119 Full papers submissions and 68 short paper submissions the program committee selected 43 Full papers and 28 Short papers for presentation at the conference. In addition, 30 posters and 14 demonstrations were selected for presentation at the special poster and demo evening session. As in previous years we will be awarding the Vannevar Bush Best Paper Award (sponsored by ACM). In addition to the main meeting, a full schedule of tutorials and workshops has been arranged bracketing the main meeting. This year we also host the largest Doctoral Consortium yet held at JCDL, where student researchers in digital library topics work with an international panel of faculty mentors on exploring and refining their thesis research.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.265 | 0.155 |
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".