Bibliographic record
Abstract
In managing an academic conference, the program chair (PC) is required to deal with many repetitive administrative tasks such as: interaction with authors and program committee members (reviewers), paper collection, paper allocation, distributing the paper to the reviewers, collating, sorting and tabulating the evaluations, orchestrating the debate of controversial evaluation of some of the papers, making the final tabulation and preparing the notification and comments to the reviewers and authors. The Conference Management System (ConfSys) presented here is a entirely a Web-based system which provides facility for the program chair(s) to set up the details for a meeting, allows authors to register and submit papers to the system on-line; records the topic of expertise of the members of the program committee members (reviewers); helps the PC by performing an automatic allocation of the submitted papers to the reviewers. The reviewers have a facility to bid in an auction for papers to review and later to download and review the assigned paper via the Internet. The ConfSys uses the DBLP database in the automatic assignment of papers to avoid any conflict of interest and thus helps in the allocation of papers to the reviewers for a fair and impartial review of each paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.169 | 0.123 |
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".