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Record W4320737743 · doi:10.1145/3584667.3584669

SIGCSE Technical Symposium 2023: Information for Attendees

2023· article· en· W4320737743 on OpenAlexaboutno aff
Maureen Doyle, Ben Stephenson, Brian Dorn, Leen‐Kiat Soh, Lina Battestilli, K Stephens, Delaram Yazdansepas

Bibliographic record

VenueACM SIGCSE Bulletin · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsCraftConventionComputer scienceLibrary scienceWork (physics)Engineering ethicsPolitical scienceEngineeringLawHistory

Abstract

fetched live from OpenAlex

The 2023 SIGCSE Technical Symposium will take place from March 15 - 18, 2023, at the Metro Toronto Convention Centre in Toronto, Canada. We look forward to welcoming you to Canada for the first ever SIGCSE Technical Symposium outside of the United States. The program for the 2023 Technical Symposium is online now. It is a diverse program that showcases the tremendous work done by so many researchers and educators to improve our craft. We truly believe that it has something for everyone and that your biggest challenge will be identifying which sessions you want to attend when there are so many exceptional options.

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 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)0.007
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.023

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 head0.107
GPT teacher head0.376
Teacher spread0.269 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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