Bibliographic record
Abstract
There were five finalists for the 2022 MCDM Junior Researcher Best Paper Award of the MCDM section of INFORMS. Finalists were Sune Lauth Gadegaard, Aarhus University, Denmark; Eleftherios Siskos, Paul Scherrer Institut Switzerland; Merve Bodur, University of Toronto, Canada; and Mohammad Ghaderi, Pompeu Fabra University, Spain (the winner). Jiapeng Liu, Xi’an Jiaotong University, China could not attend the meeting. Roman Słowiński was the 2022 Jury Chair, Poznań University of Technology All 5 finalists received a diploma at the business meeting of the MCDM Section of INFORMS on October 16, 2022. The winner selected by the Jury was Mohammad Ghaderi. This was a great success for the MCDM/A community to receive ten nominations and to have five finalists for this award!
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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.236 | 0.182 |
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".