Proceedings of the Thirteenth Workshop on Trends in Functional Programming in Education
Bibliographic record
Abstract
This volume of the Electronic Proceedings in Theoretical Computer Science (EPTCS) contains revised selected papers that were initially presented at the 13th International Workshop on Trends in Functional Programming in Education (TFPIE 2024). This workshop was held at Seton Hall University in South Orange, NJ, USA on January 9, 2024. It was co-located with the 25th International Symposium on Trends in Functional Programming (TFP 2024), which took place on January 10-12, 2024. The goal of TFPIE is to gather researchers, teachers, and professionals that use, or are interested in the use of, functional programming in education. TFPIE aims to be a venue where novel ideas, classroom-tested ideas, and works-in-progress on the use of functional programming in education are discussed. TFPIE workshops have previously been held in St Andrews, Scotland (2012), Provo, Utah, USA (2013), Soesterberg, The Netherlands (2014), Sophia-Antipolis, France (2015), College Park, MD, USA (2016), Canterbury, UK (2017), Gothenburg, Sweden (2018), Vancouver, Canada (2019), Krakow, Poland (2020), online due to COVID-19 (2021, 2022, with some talks from TFPIE 2022 also presented in person at the Lambda Days in Krakow, Poland), and Boston, MA, USA (2023, back in-person).
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 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.094 | 0.028 |
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".