Shadow Program Committee Initiative
Bibliographic record
Abstract
The Shadow Program Committee (PC) is an initiative/program that provides an opportunity to Early-Career Researchers (ECRs), i.e., PhD students, postdocs, new faculty members, and industry practitioners, who have not been in a PC, to learn rst-hand about the peer-review process of the technical track at Software Engi- neering (SE) conferences. This program aims to train the next generation of PC members as well as to allow ECRs to be recog- nized and embedded in the research community. By participating in this program, ECRs will have a great chance i) to gain expe- rience about the reviewing process including the restrictions and ethical standards of the academic peer-review process; ii) to be mentored by senior researchers on how to write a good review; and iii) to create a network with other ECRs and senior researchers (i.e., Shadow PC advisors). The Shadow PC program was rst introduced to the SE research community at the Mining Software Repositories (MSR) confer- ence in 2021. The program was led by Patanamon Thongta- nunam and Ayushi Rastogi (Shadow PC Co-chairs) with support from Shadow PC Advisor Co-Chairs (Foutse Khomh and Serge Demeyer), PC Co-Chairs of the technical track (Meiyappan Na- gappan and Kelly Blincoe), and the General Chair of the con- ference, Gregorio Robles. To promote and facilitate the Shadow PC program at SE conferences in the future, this report provides details about the process and a re ection on the Shadow PC pro- gram during MSR2021. The presentation slides and video are also available online at https://youtu.be/ReUXwmtIEk8.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.380 | 0.242 |
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".