Replication Data and Analysis Code for: Goal-Oriented Modeling and Analysis of Explanation Requirements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Replication data of our experimental study evaluating a framework for capturing and implementing explanation requirements. The data consist of 20 data points from an equal number of participants. The participants were presented two cases of software-intensive systems: a socio-technical system, namely a University Petitions case, and an AI-based system, namely Home Automation case. They performed two main tasks. Firstly, they were presented with logs from activity within the context of those systems. They were asked which of the listed actions are explanation-worthy / answering on an 5-point ordinal scale. Secondly, for selected actions, they were then presented with 2-4 statements meant to explain the action. They where asked to rank the explanations and also rate them (5-point ordinal scale) with respect to the usefulness of the explanation. This data package contains the responses (data.xlsx and data.cvs) to be interpreted based on survey.txt, the psytoolkit script used to generate the survey. An Analysis.Rmd script performs the data analysis presented in the paper.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it