A new model for semantic photograph description combining basic levels and user-assigned descriptors
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
Few studies have been conducted to identify users’ desired semantic levels of image access when describing, searching, and retrieving photographs online. The basic level, or the level of abstraction most commonly used to describe an item, is a cognitive theory currently under consideration in image retrieval research. This study investigates potential basic levels of description for online photographs by testing the Hierarchy for Online Photograph Representation (HOPR) model, which is based on a need for a model that addresses users’ basic levels of photograph description and retrieval. We developed the HOPR model using the following three elements as guides: the most popular tags of all time on Flickr, the Pyramid model for visual content description by Jörgensen, Jaimes, Benitez, and Chang, and the nine classes of image content put forth by Burford, Briggs, and Eakins. In an exploratory test of the HOPR model, participants were asked to describe their first reaction to, and possible free-text indexing terms for, a small set of personal photographs. Content analysis of the data indicated a clear set of user preferences that are consistent with prior image description studies. Generally speaking, objects in the photograph and events taking place in the photograph were the most commonly used levels of description. The preliminary HOPR model shows promise for its intended utility, but further refinement is needed through additional research.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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