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
This paper tells the story of an arts-informed, visual study−the iSquare Research Program−and the four visual analysis techniques that have been used across its history: compositional interpretation, thematic analysis, pictorial metaphor analysis, and content analysis. When each analytical strategy was applied, in turn, to the visual data set of more than 2,000 original drawings, different insights about the target subject of ‘information’ came into view. To begin, the iSquare Research Program is introduced and placed in the disciplinary context of information science. One at a time, the research questions that emerged in the project and their complementary analytical strategies are outlined, with attention to matters of implementation, interpretation, and results. By the conclusion, readers will be able to distinguish and compare the four visual analysis techniques and can thereafter synchronize one or more to their own research projects and questions. Overall, what follows is an adventure story about the selective focusing power of analytic lenses and their ability to generate myriad discoveries within a singular visual data set.
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 | 0.000 |
| 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