Bibliographic record
Abstract
In seeking for an understanding of ethical practices in health care situations, our challenge is always both to recognize and respond to the call of individuals in need. In attuning ourselves to the call of the vulnerable other an ethical moment arises. Asking 'how are you?' in health care practice is our very first possibility to learn how a particular person finds herself or himself in this particular situation. Here, 'how are you?' shows itself as an ethical question that opens up a relational space that calls forth a response. It is a way to understand the situated moments in which we are already that enables us to act respectfully. Our ethical frameworks assist us in trying to decide what is the right thing to do given a set of circumstances. Yet there is a prior step that already calls us to ethical attention; this is when we ask 'how are you?', which transforms a seemingly small interaction into an ethical moment. 'How are you?' is a question that turns us back to who we are as health care professionals and calls us to be more deeply attentive to the moment. When we sincerely ask 'how are you?' we enact our ethical commitments to one another.
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".