How can Rorty help nursing science in the development of a philosophical ‘foundation’?
Bibliographic record
Abstract
What can nurse scientists learn from Rorty in the development of a philosophical foundation? Indeed, Rorty in his 1989 text entitled Contingency, Irony, and Solidarity tantalizes the reader with debates of reason 'against' philosophizing. Forget truth seeking; move on to what matters. Rorty would rather the 'high brow' thinking go to those that do the work in order to make the effort useful. Nursing as an applied science, has something real that is worth looking at, and that nurse researchers need to think about. And as a profession built upon relationships, we should be thinking of the exchanges we have with those around us, of the contrasts in vocabularies used and of the contingencies involved, letting this launch us into our imaginings and areas of enquiry. The business of nurse researchers is to study what nurses do--how we care; Rorty would have us care. But, not to dismiss the reflective thinker as Rorty advocates for the self-doubting ironist to continue to seek the final vocabulary, the ideal of what 'this' means, accepting this as the best to be offered at the time. As a science struggling to find foundation, we need only to look at what we do and value--as antifoundational as Rorty portrays himself, Rorty 'ironically' may have revealed a foundation for nursing science that is consistent with its path.
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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 | 0.064 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.021 | 0.049 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.020 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".