Bibliographic record
Abstract
This paper reflects upon the conditions how ‘nudging’ can change individual health choices without being paternalisticand therefore can be defined as an instrument of social justice? So many problems we are facing in today’s nursing aresituated at the intersection of autonomy and heteronomy, i.e. why well informed and autonomous people make unhealthylifestyle choices. If people do not choose what they want, this is not simply caused by their lack of character or capability,but also by the fact that absolute autonomy is impossible; also autonomous individuals are ‘contaminated’ byheteronymous aspects, by influences from ‘outside’. In an earlier article I made an analysis of my neologism ‘oughtonomy’to support the thesis that when it comes down to human existence, autonomy and heteronomy are intertwined, more thanthey are merely opposites.Although nudging might be of help in many nursing settings, we should evaluate it with the same criticism as we judgeupon paternalism. Despite the potential of nudging for nursing, there is a risk to put the nurse again in the position of thepaternalistic outsider who knows how people should behave. But maybe the awareness of the oughtonomous decisions weall make in our lives, can help us to understand why people act mindless in some situations or why we choose what wechoose. Knowing this is one thing, giving people the authority of an expert to know what is better off for others, another.Despite the potential of the last, the former concept does not legitimate paternalistic interferences in patient’s lifestyle.
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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".