Eye opener: exploring complexity using rich pictures
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
Historically, approaches to exploring complexity have mainly focused on the notion that complex problems must be deconstructed into simpler parts if we are to make sense of them; this is the so-called reductionist approach. When dealing with the complexity of human experience, however, deconstructing the experience without diminishing it is a daunting, perhaps impossible task. Researchers wishing to make sense of complex experiences often begin by interviewing the individuals at the centre of those experiences. But interviews can be frustratingly limited. Visual methods, such as drawings, are beginning to show promise for designing research that taps into the complexity of professional practice. The promise of visual methods may relate to a key notion in complexity research: 'disruptions'. In this paper I introduce the notion of 'disruptions' as articulated by complexity approaches from 'systems engineering' and suggest 'rich pictures' as an effective visual method to describe and understand complex problems in medical education 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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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