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
The first step in writing code is understanding the problem to be solved. When this step is not properly completed, students can waste time developing a solution to the wrong problem. Arguably, this tendency is exacerbated by online automatically-tested code submission systems where students work in isolation and sometimes appear to focus more on passing the instructor testcases than on understanding the problem or its solution. We report on an randomized A/B test with 831 CS1 students using an online submission system. Students in the control group wrote small Python functions based on a written description including a docstring with one example. Before the treatment-group students solved the same exercise, they were given a description of the same functions and were asked to provide the corresponding output for three sets of input. We hypothesized that this would decrease the time and attempts required to correctly write the code because students in the treatment group would not waste time on an incorrectly-conceived problem. We found support for this hypothesis on one of the problems but not on the other, and we offer some suggestions as to how this might be explained.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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