Bibliographic record
Abstract
Catherine Fraser is an artist, an art therapist in private practice, and a public health nurse with the Vancouver Island Health Authority in Duncan, British Columbia, Canada. A professional artist since 1983, Fraser works in a variety of media, including painting, photography, clay sculpture, and mixed media. She has had 29 one-woman shows, and her art has appeared in many juried shows and is in private and public collections worldwide. At the opening of her recent solo show in Copenhagen, Denmark, she spoke about how she uses creativity in her practices as artist, art therapist, and nurse. AJN has previously featured her work on the cover and in Art of Nursing in August 2007. This piece is from a series of touch drawings called “Body Portraits.” The method, which was developed by Deborah Koff-Chapin (http://touchdrawing.com), involves placing paper over wet paint and then moving the fingers and hands over the paper's surface to create images on the underside. Fraser writes: “It's a soulful way of working and can be used in various settings. I often do touch drawings as a meditative practice. The images in ‘Body Portraits’ were made while I was waiting to get an MRI, and found myself reflecting on the human body.” Body Portrait 1 © 2015 by Catherine Fraser. For a short video on Fraser's work, visit www.youtube.com/watch?v=xsoT-Yu4-Jw. To see more and to contact the artist, visit her Web site: www.catherinefraserart.com. Art of Nursing is coordinated by Sylvia Foley, senior editor: [email protected].
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.303 | 0.101 |
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".