Bibliographic record
Abstract
In the early months of 2020, the COVID-19 pandemic abruptly transformed the way the world works and collaborates. With most workrelated travel curtailed and many knowledge workers constrained to work-from-home, face-to-face interaction was replaced by a world of virtual communication and collaboration. In 2021, workflows continue to evolve for universities, corporations, and governments to support "socially distant" R&D, education, and organizational infrastructure. This paper reports on a ICSE 2021 workshop panel focused on how COVID-19 has inspired changes to university-company collaborations, for better or worse. The panel was organized and moderated by Steven Fraser (Innoxec) with invited panelists Sheri Brodeur (MIT), Randy Katz (UC Berkeley), Xue [Steve] Liu (McGill), Stefanie Molthagen- Schnöring (HTW-Berlin), and Sheng-Ying [Aithne] Pao (NTHU Taiwan).
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.006 | 0.059 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".