Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it