Bibliographic record
Abstract
It has been said that a picture says a thousand words, that art should speak for itself. Within the social sciences, there is recognition that images are not merely illustrations, but “texts” that can be read, studied and interpreted in different ways: they are visual narratives. When we look at a work of art, we respond with our own thoughts, feelings and ideas about what it communicates. When we look at a portrait specifically, we are not just looking at a picture of an individual, we are looking at a picture of someone being looked at. It is a visual record of an interaction, as much as a likeness of the person. The artist-sitter relationship has much in common with the doctor patient relationship involving trust, attention, and an openness to ambiguity and creativity. As clinicians that are tired and feeling overwhelmed, we may objectify patients. Engaging with art can help hone our skills to consistently see the whole person. It provides freedom to sit with ambiguity and maintain curiosity and can help us become more flexible in our thinking, to hold multiple possibilities in mind at the same time. Viewing art in a group provides opportunities to understand and appreciate others’ perspectives. Drawing on multiple portraiture projects related to pediatric epilepsy, youth mental health and dementia, this presentation will provide constructive ways in which portraiture can be used to foster humanistic, patient centred care, and to understand the power of distributed cognition.
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.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".