Sadownik, S.A. (2022). Correlations on PeppeR for Time Spent Online with Online Activity by Graduate Students.pdf
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
Many academics and early career professionals are tasked with assessing online contributions in graduate courses in education. Various methods suggest research has considered the use of rubrics and attempted to assess the level of critical thinking presented in online discussions, course design, assignment weighting, engagement in course material, relationships between peers, language learners and different subject specialists approaches to discussion forums and in some cases interpretative flexibility of the course and technology. In this correlational study, two data sets and four hypotheses were tested with the use of a correlational matrix generator: (H1) The “Time Online” will have a statistically significant positive correlation with the “Words Written” for both data sets; (H2) The “Time Online” will have a statistically significant positive correlation with the “Notes Written” for both data sets; (H3) The “Time Online” will have a statistically significant positive correlation with the “Replies” for both data sets; (H4) The “Time Online” will have a statistically significant positive correlation with the “Notes Read” for both data sets. Results suggest one data set presented statistically significant positive correlation in each category (p < 0.01) while the other data set only presented one statistically significant category (p < 0.05) for “Notes Written”.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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