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
Abstract Network evolution is an important problem for social scientists, management consultants, and social network scholars. Unfortunately, few empirical data sets exist that have sufficient data to fully explore evolution dynamics. Increasingly, more and more online data sets are used in lieu of offline, face-to-face data. The veracities of these findings are questionable, however, because there are few studies exploring the similarity of online-offline dynamics. The IkeNet project investigated online and offline network evolution. Empirical data was collected on a group of 22 mid-career military officers going through a one-year graduate program. Data collection included email communication collected from the Exchange server, as well as self-reported friendship, and time spent together, over a course of 20 weeks. Numerous attribute data on the individual actors was collected from their military personnel files. The data allows network scholars to conduct research into the dynamics of network evolution and allows educators a real-world example data set for use in classroom instruction.
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