Bibliographic record
Abstract
Is There Unity in an Image?Several years ago, a nurse manager described her interaction with a registered nurse working in an ICU dressed in an outfit that might otherwise be described as beach ready.The nurse's response to a challenge that this might not be appropriate attire for the clinical work setting was: "Surely you don't expect me to dress like you?" Taken aback by this nurse's affront to the challenge of her garb, and in the absence of any policy detailing appropriate dress, as the manager mulled over a "suit"able response, she was most dismayed at the need to explain the merits of projecting a professional image.While many organizations, including professional associations, have articulated guidelines for an appropriate professional appearance (including attire, hair, nails and jewellery) and acceptable approaches for identifying oneself to the public (ARNNL 2013), recent decades have seen the disappearance of a consistent uniform for nurses.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".