Image use within the work task model: Images as information and illustration
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 With increasing sophistication in technology has emerged a growing interest in accessing images for personal and work purposes. In this research we investigated the use of images as data—for the information contained within the image, and as an object to illustrate. Thirty journalists and historians from academic and professional work settings were interviewed using a series of semistructured questions regarding how they use images (for information or for illustration) and the types of image attributes used to describe an appropriate image for their work. This was done within the context of a work task model used by this group to understand how images are used throughout the process of completing a typical written work task. Findings suggest that the stage of the work task process has a significant impact on how the image is used (information or illustration). Participants use as many descriptive as conceptual image attributes to locate an image, but, interestingly, there are no significant differences according to use for information or illustration purposes. This study increases our understanding of the function of images in the written work task process, and provides new knowledge about the conceptual and descriptive attributes that are most valued.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.010 |
| 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