SIGCSE Speaker Fund Report: Funding for Pre-Conference Workshop at CCSC-CP 2024
Bibliographic record
Abstract
On April 5, 2024, the SIGCSE Speaker Fund supported Mr. Carter Zenke of Harvard University and Mr. Charlie Liu of Yale University who conducted a 3-hour pre-conference workshop entitled "Distributing, Collecting, and Autograding Assignments with GitHub Classroom" at the Consortium for Computing Sciences in Colleges - Central Plains Region (CCSC-CP) Conference at Graceland University, Lamoni, Iowa. The presenters shared their knowledge, expertise, and classroom material (CS50 at Harvard) on GitHub Classroom with the audience. There were about 28 participants in the workshop. Mr. Zenke, Mr. Liu, and other colleagues had previously presented and conducted the workshop at SIGCSE 2023 on March 17, 2023, in Toronto, Canada.
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How this classification was reachedexpand
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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; both teacher heads agree on what is shown here.
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".