Comparing Block-Based Programming Models for Two-Armed Robots (supplementary materials)
Bibliographic record
Abstract
Supplementary Material for the research paper "<strong>Comparing Block-Based Programming Models for Two-Armed Robots</strong>", published in <em>Transactions on Software Engineering (TSE)</em> in 2020. Paper DOI: 10.1109/TSE.2020.3027255 This archive contains: A CSV and an HTML version of the raw data collected in the comparative study described in the paper. Note that the first three rows of both files are headers that contain the original questions and comments to allow an interpretation of the coded responses. Also note that some raw text data has been stripped as it might contain information that could be used to identify or de-anonymize individual participants or their responses. A PDF copy of the survey used in the study. Note that the survey contains two short demonstration videos that were embedded directly from youtube and can be found using the following links: https://www.youtube.com/watch?v=uI0bWswP-Ew https://www.youtube.com/watch?v=yThGW_C83Jw The PDF copy of the survey is linearized and contains all possible branches of the survey. The four possible (randomly assigned) orders of survey blocks were: Intro -> Consent -> Video -> Survey A-1 -> Survey A-2 -> Survey A-3 -> Survey A-4-1 -> Survey A-5-1 -> Post-Survey Intro -> Consent -> Video -> Survey A-1 -> Survey A-2 -> Survey A-3 -> Survey A-4-2 -> Survey A-5-2 -> Post-Survey Intro -> Consent -> Video -> Survey B-1 -> Survey B-2 -> Survey B-3 -> Survey B-4-1 -> Survey B-5-1 -> Post-Survey Intro -> Consent -> Video -> Survey B-1 -> Survey B-2 -> Survey B-3 -> Survey B-4-2 -> Survey B-5-2 -> Post-Survey
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".