Bibliographic record
Abstract
I turn to Confucius for guidance in the writing of this editorial about certain untruths relating to palliative care.Confucius established a link between word, thought, and deed; between language, meaning, and reality.That link is found in this Confucian saying: "If names be not correct, language is not in accordance with the truth of things.If language be not in accordance with the truth of things, affairs cannot be carried on to success" (1).If the link between language, meaning, and reality is confusion and distortion rather than clarity and correspondence, then a cycle of decline starts.If we do not say what is meant, what should not be done is advanced as a solution, and what should be done remains undone.If the cycle continues unchecked, reality becomes chaotic.Each deed expressing a confused and distorted view of the world further corrupts the words, thoughts, and meanings with which we struggle to find where we are and where we should be going (2).Certain untruths about palliative care involve not only a flawed understanding of palliative care but also distort the actual giving of care to the very sick, to the suffering, to the dying.It is an ethical and human imperative to identify and to expose these untruths, and this editorial is only a beginning.
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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.050 | 0.064 |
| Insufficient payload (model declined to judge) | 0.012 | 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".