Dramatic changes to well-known places go unnoticed
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
How well do we know our city? It turns out, much more poorly than we might imagine. We used declarative memory and eye-tracking techniques to examine people’s ability to detect modifications of landmarks in Toronto locales with which they have had extensive experience. Participants were poor at identifying which scenes contained altered landmarks, whether the modification was to the landmarks’ relative size, internal features, or surrounding context. To determine whether an indirect measure would prove more sensitive, we tracked eye movements during viewing. Changes in overall visual exploration, but not to specific regions of change, were related to participants’ explicit endorsement of scenes as modified. These results support the contention that very familiar landmarks are strongly integrated within the spatial context in which they were first experienced, so that any changes that are consciously detected are at a global or coarse, but not local or fine-grained, level.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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