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 Could we prevent the death of a friend by travelling in time? Claims like this are usually settled by considering the structure of time: we could prevent their death provided time branches; we could not prevent their death if time is linear. Branching time allows changing timelines, and on some branches we might prevent their death. However, what would we say about the structure of time if, despite efforts under time-travel and repeated changes to the past, we continually failed to save our friend? In many time-travel fictions, certain events robustly reoccur across different branches. Some propositions, deaths especially, have an attraction despite changes to the past. In this essay I apply the notion of attractor from dynamical system theory to the set of possible worlds used to interpret a proposition within a Kripke model. A proposition is an attractor if there is a region of the model where the accessible worlds lead invariably to the extension of the proposition. Accessibility relations can have inevitable asymptotic structure. I argue that treating a proposition as an attractor in a Kripke model is a good way to provide an account of the inevitability of events.
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 | 0.001 |
| 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) | 0.001 | 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