Message from the MSR 2023 Junior PC Co-Chairs
Bibliographic record
Abstract
Following two successful editions of the MSR Shadow PC in 2021 and 2022, MSR 2023 integrated the junior reviewers into the main Technical Track Program Committee.The main goal remains unchanged: to train the next generation of MSR (and, more broadly, software engineering) reviewers and program committee members, in response to a widely-recognized challenge of scaling peer review capacity as the research community and volume of submissions grows over time.As with the previous Shadow PC, the primary audience for the Junior PC is early-career researchers (PhD students, postdocs, new faculty members, and industry practitioners) who are keen to get more involved in the academic peer-review process but have not yet served on a technical research track program committee at big international SE conferences (e.g., ICSE, ESEC/FSE, ASE, MSR, ICSME, SANER).Serving on the Junior PC is an excellent opportunity for early-career researchers (PhD students, postdocs, new faculty members, industry practitioners, etc) to be recognized and gain experience in community service, i.e., being part of a major conference program committee.It is also worthwhile for a number of reasons, including:• (New) Seeing examples of actual reviews for the same papers, written by the regular PC members, gaining more experience as a reviewer and learning from the senior researchers how to write better reviews.• (New) Participating in the real review process and contributing to the final outcome of the paper, thus improving the motivation and engagement of the junior reviewers throughout the process.• Getting to know how a PC is run and how it operates.• Being mentored by experienced PC members.• Gaining experience reviewing papers and understanding the challenges faced by reviewers reading multiple papers which may not always be in their area of expertise.• Submitting high-quality reviews makes a junior reviewer a more likely candidate for future PCs of the technical track of MSR or otherwise.• Getting to see both strong and weak papers at the submission stage.• Discovering what it takes to publish a paper in a reputable conference, such as MSR.• Having a chance to read top-notch papers in your area of expertise before they are published. Recruitment and Selection ProcessWe used self-nomination as a recruitment process for Junior PC.We ran a one-month advertisement campaign (October 19th, 2022 -November 20th, 2022, AoE) through various social media channels e.g., SEWORLD, Twitter, and Facebook.Then, the formal invitation email was sent out on December 1st, 2022.We were overwhelmed with the positive reaction from the community, with a total of 197 nominations for the Junior PC.This is 77.5% considerably higher than 111 MSR 2022's Shadow PC applications and 21.6% higher than 162 MSR 2021's Shadow PC applications.Unfortunately, we did not have the capacity to accept everyone.After carefully reviewing each nomination, we selected 107 applicants for the MSR 2023 Junior PC, giving priority to applicants in the later stages of their PhD program and ensuring that we put together a diverse Junior PC that reflects the geographic, demographic, and research area diversity of the MSR community.Below is the summary statistics of the MSR Junior PC:• Acceptance Rate: 107 Junior PC members out of 197 applications (54% acceptance rate) are accepted.• Gender Diversity: 34% are woman/non-binary, which is higher than the ratio of the total applications (27%).
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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.036 | 0.043 |
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".