Dataset for "Information Correspondence between Types of Documentation for APIs"
Bibliographic record
Abstract
This online appendix contains the coding guide and the data used in the paper Information Correspondence between Types of Documentation for APIs accepted for publication in the Empirical Software Engineering (EMSE) journal. The tutorial data was retrieved in October 2018. It contains the following files: 1. CodingGuide.pdf: the coding guide to classify a sentence as API Information or Supporting Text. 2. annotated_sampled_sentences.csv: the set of 332 sampled sentences and two columns of corresponding annotations – one by the first author of this work and the second by an external annotator. This data was used to calculate the agreement score reported in the paper. 3. - .csv: the data set of annotated sentences in the tutorial on in . For example Python-REGEX.csv is the file containing sentences from the Python tutorial on regular expressions. This file contains the preprocessed sentences from the tutorial, their source files, and their annotation of sentence correspondence with reference documentation. For licensing reasons, we are unable to upload the original API reference documentation and tutorials, however these are available on request.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.095 |
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".