The effect of conference proceedings on the scholarly communication in Computer Science and Engineering
Bibliographic record
Abstract
Conference papers have traditionally been a quick form of research communication, and an important source of information for scientists in addition to the standard journal papers. However, in the disciplines of Computer Science and Engineering, a vast majority of the peer-reviewed publications is communicated in the form of conference papers, and conference proceedings have become the primary channel of research communication in these disciplines. While this form of scholarly communication was effective for Computer Science as a young discipline, it introduces several limitations that make it non-optimal for a mature and established scientific field. These include the quality of the peer-reviewed work, selection of papers for publication, and also the efficacy of conferences as forums for expressing innovative and visionary ideas and providing opportunities for networking and meeting other researchers in the field. Here we review the differences between Computer Science and Engineering conference publications and the traditional journal publication used in other scientific disciplines, and discuss the effect of these differences on the scholarly communication in this field.
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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 | 0.092 | 0.550 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.029 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".