Proceedings of the 2008 international workshop on Functional and declarative programming in education
Bibliographic record
Abstract
The 2008 edition of the International Workshop on Functional and Declarative Programming in Education (FDPE 2005), took place in Victoria, British Columbia, Canada on September 21, 2008 in conjunction with the International Conference on Functional Programming (ICFP 2008). Functional and declarative programming plays an increasingly important role in computing education at all levels. This workshop aimed at bringing together educators and others who are interested in exchanging ideas on how to use a functional or declarative programming style in the classroom. Previous workshops have been held in Tallin (2005), Pittsburgh (2002), Paris (1999), and Southampton (1997). The call for papers attracted 13 submissions. Each paper was reviewed by at least three members of the international program committee. During a three-day electronic meeting, the program committee selected nine of the submissions for publication and presentation at the workshop. Beside these presentations, the workshop program also included a discussion about new ideas for teaching (functional and declarative) programming at universities.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.012 |
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".