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 issue represents a bit of an experiment. Our last Symposium for SoTL, in November 2022, seemed particularly thought-provoking and we were proud of the lively community and conversations so evident throughout the conference. We decided that we weren’t quite ready to let these conversations go, and invited each keynote speaker to provide an invited piece for this issue. We then created a call for proposals inviting conference attendees to respond to the keynotes, and in this issue are two pieces that do just that. In each case, the authors take the ideas they understood from one of the keynotes and develop and relate them to their own ideas and contexts. A signature element of our Symposium has become pop-up poetry. As part of this Call and Response issue, we also offer two essays written by the poets, Richard Harrison and Beth Everest, as well as two responses to the experience. We hope you enjoy reading this issue as much as we enjoyed putting it together. It seems a perfect segue into our next Symposium, happening in just a few week’s time, where we will continue the conversation.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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