IEEE-CS/ACM computing curricula
Bibliographic record
Abstract
article Share on IEEE-CS/ACM computing curricula: computer engineering & software engineering volumes Authors: John Impagliazzo Hofstra University, Hempstead, NY Hofstra University, Hempstead, NYView Profile , Esther A. Hughes Virginia Commonwealth University, Richmond, VA Virginia Commonwealth University, Richmond, VAView Profile , Richard LeBlanc Georgia Tech, Atlanta, GA Georgia Tech, Atlanta, GAView Profile , Tim Lethbridge University of Ottawa, Ottawa, Ontario, Canada University of Ottawa, Ottawa, Ontario, CanadaView Profile , Andrew McGettrick University of Strathclyde, Glasgow, United Kingdom University of Strathclyde, Glasgow, United KingdomView Profile , Ann E. K. Sobel Miami University, Oxford, OH Miami University, Oxford, OHView Profile , Pradip K. Srimani Clemson University, Clemson, SC Clemson University, Clemson, SCView Profile , Mitchell D. Theys University of Illinois at Chicago, Chicago, IL University of Illinois at Chicago, Chicago, ILView Profile Authors Info & Claims ACM SIGCSE BulletinVolume 36Issue 1March 2004 pp 450–452https://doi.org/10.1145/1028174.971453Published:01 March 2004Publication History 0citation798DownloadsMetricsTotal Citations0Total Downloads798Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.568 | 0.404 |
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".