Analyse eines Bürgerbeteiligungsverfahrens zu ethisch-politischen Fragen der Verteilung von Gesundheitsgütern- Vergelich der inhaltlichen Ergebnisse der Lübecker Konferenz mit einer kanadischen citizens jury zu diesem Themenkompomplex
Bibliographic record
Abstract
A consensus conference took place in Lübeck 2010 to the subject priority setting in health care. 20 citizens of different age and with heterogeneous educational backgrounds found out about priority setting in health care, discussed her convictions and experiences and wrote a common vote. Priority setting in health care was defined as a construct of thoughts which values and criteria in the medical care are really important, and which seem less important. From these considerations orders of rank of medical interventions and therapies can be compiled. This paper answered following questions: Can citizens have a good discourse about such a complicated subject like priority setting in health care? Which content results they have? Which meaning can these results have for the whole debate? The citizens had a successful discourse what is worked out in this paper on criterias of the German philosopher Jürgen Habermas. With this method to analyse a consensus conference, a new way was walked within here. Moreover, it is shown with regard to the content discussion which important meaning the civil discourse has for the whole debate. The citizens have appealed to single new aspects. In the analysis appears that the Lübeck citizens put a main focus with values and criteria which concern the individual. The comparison of the content results of the Lübeck consensus conference with the content results of a Canadian Citizen jury to this subject complex shows many parallels and gives the instruction to the fact that a land-covering common will of citizens exists.
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 | 0.065 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".