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
Effective street peddlers monitor passersby, where they tune their message to capture and keep the passerby's attention over the entire duration of the sales pitch. Similarly, advertising displays in today's public environments can be more effective if they were able to tune their content in response to how passersby were attending them vs. just showing fixed content in a loop. Previously, others have prototyped displays that monitor and react to the presence or absence of a person within a few proxemic (spatial) zones surrounding the screen, where these zones are used as an estimate of attention. However, the coarseness and discrete nature of these zones mean that they cannot respond to subtle changes in the user's attention towards the display. In this paper, we contribute an extension to existing proxemic models. Our Peddler Framework captures (1) fine-grained continuous proxemic measures by (2) monitoring the passerby's distance and orientation with respect to the display at all times. We use this information to infer (3) the passerby's interest or digression of attention at any given time, and (4) their attentional state with respect to their short-term interaction history over time. Depending on this attentional state, we tune content to lead the passerby into a more attentive stage, ultimately resulting in a purchase. We also contribute a prototype of a public advertising display -- called Proxemic Peddler -- that demonstrates these extensions as applied to content from the Amazon.com website.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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