Bibliographic record
Abstract
We are pleased to welcome you to the 2009 ACM-SIGPLAN Workshop on ML -- ML 2009. ML is a family of programming languages that includes dialects known as Standard ML, Objective Caml, and F#. The development of these languages has inspired a large amount of computer science research, both practical and theoretical. This workshop aims to provide a forum to encourage discussion and research on ML and related technology, that is, higher-order, typed, or strict programming languages. The 2009 Workshop on ML is held in conjunction with the 14th ACM-SIGPLAN International Conference on Functional Programming (ICFP 2009) in Edinburgh, Scotland, UK. Previous instances were ML 2005 in Tallinn, Estonia, ML 2006 in Portland, Oregon, USA, ML 2007 in Freiburg, Germany, and ML 2008 in Victoria, British Columbia, Canada). The call for papers attracted 11 submissions from Europe, the United States, Asia, and New Zealand. Each paper was reviewed by at least three international referees. After a 4-day electronic meeting, the program committee accepted 6 papers for presentation at the workshop. One PC submission was received but not accepted. In addition to regular papers, this year's workshop features an informal demo track for live demonstrations of software written in or for ML. We received 11 submissions of demo proposals, out of which the program committee selected the best for presentation. The program committee is also happy to have an invited talk by Cedric Fournet, researcher in security and distributed systems in the Programming Principles and Tools group at Microsoft Research Cambridge, and project leader at the MSR-INRIA Joint Center in Orsay, France. An abstract of his talk is included in these proceedings. Finally, we will have a panel discussion on Future Directions of ML, to which we invited leading researchers in the field. It is our hope that it will inspire fruitful discussion about future directions for research into ML and evolution of the ML family of programming languages.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.117 | 0.042 |
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".