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
This volume contains seven lectures held at the University of Cologne in the winter semester of 2021/22 and the summer semester of 2022 as part of a lecture series organised by the Research Unit "Law and Ethics of Digital Transformation" under the general heading "The Power of Algorithms". The lectures deal with both basic questions and current issues of the digital transformation. The authors have been working on forward-looking topics for many years. <bold>With contributions by</bold> Nina Eckertz | Prof. Dr. Øyvind Eide | Prof. Dr. Dr. h.c. Dr. h.c. Stephan Hobe, LL.M. (McGill) | Dr. Amina Hoppe | Prof. Dr. Christian Katzenmeier | Prof. Dr. Torsten Körber, LL.M. | Prof. Dr. Axel Ockenfels | AkadR a.Z. Dr. Martin Schwamborn | Prof. Dr. Dr. h.c. Martin Paul Waßmer
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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