Image use within the work task model: Images as information and illustration
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".