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Record W4210630911 · doi:10.1145/3502771.3502776

Strategies for "Socially Distant"

2022· article· en· W4210630911 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueACM SIGSOFT Software Engineering Notes · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
Fundersnot available
KeywordsFace (sociological concept)Coronavirus disease 2019 (COVID-19)WorkflowWork (physics)PandemicManagementPublic relationsEngineeringPolitical scienceFace-to-faceKnowledge managementSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.216
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it