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
Purpose Arts-informed, visual research was conducted to document the pictorial metaphors that appear among original drawings of information. The purpose of this paper is to report the diversity of these pictorial metaphors, delineate their formal qualities as drawings, and provide a fresh perspective on the concept of information. Design/methodology/approach The project utilized pre-existing iSquare drawings of information that were produced by iSchool graduate students during a draw-and-write activity. From a data set of 417 images, 125 of the strongest pictorial metaphors were identified and subjected to cognitive metaphor theory. Findings Overwhelmingly, the favored source domain for envisioning information was nature. The most common pictorial metaphors were: Earth, web, tree, light bulb, box, cloud, and fishing/mining, and each brings different qualities of information into focus. The drawings were often canonical versions of objects in the world, leading to arrays of pictorial metaphors marked by their similarity. Research limitations/implications Less than 30 percent of the data set qualified as pictorial metaphors, making them a minority strategy for representing information as an image. The process to identify and interpret pictorial metaphors was highly subjective. The arts-informed methodology generated tensions between artistic and social scientific paradigms. Practical implications The pictorial metaphors for information can enhance information science education and fortify professional identity among information professionals. Originality/value This is the first arts-informed, visual study of information that utilizes cognitive metaphor theory to explore the nature of information. It strengthens a sense of history, humanity, nature, and beauty in our understanding of information today, and contributes to metaphor research at large.
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) | 0.001 | 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