Bibliographic record
Abstract
Florence Nightingale lived and worked in response to her times--yet also ahead of her time. She insisted on pursuing a career even though her wealthy family could have provided her with a lifetime of leisure. Because she was a woman, this choice to work outside her home was all the more unusual. Nightingale was also a vanguard woman because she chose nursing, a role that was considered the work of desperate, impoverished women who lived on the street like prostitutes. In addition to these unusual choices, Nightingale's career was unique beyond anyone in her time. She was one of the most prolific authors of the 19th century. In addition to being an early role model for nursing, Nightingale was also a leader in several other fields emerging in her time, including social work, statistical analysis, and print journalism. As a global thinker, Nightingale would have loved 21st century. She noted cultural, social, and economic concerns, particularly in relation to health and to the discipline of nursing. She urged nurses to progress in their practice and to think outside their official domains. She responded to the culture of the 19th century by envisioning what could be changed. Working with her talents and available resources, she evolved the health care culture of the 20th century and beyond. She called all of this work "Health-Nursing." As we remember and further study the extraordinary panorama that is our Nightingale legacy, we are creating and shaping our relevant, emerging 21st century nursing practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".