Die wertebedingte Krise des Ökosystems. Eine Verbindung systemtheoretischer und wertphilosophischer Überlegungen
Bibliographic record
Abstract
Historically, crises such as the COVID-19 pandemic have prompted politicians to break up dead-locked structures and implement far-reaching reforms. Path dependencies can be interrupted in times of crisis. This volume examines the social impact of the current pandemic as well as both the long-term challenges it poses and the potential it offers from the perspective of economic and business ethics. How has the COVID-19 crisis changed the balance of power between the state, markets and business? What are the obligations of companies during a pandemic? To what extent are the fight against the coronavirus crisis and that against the climate crisis compatible? What role can and should business ethics play in times of crisis? With contributions by Prof. Dr. Michael S. Aßländer; Prof. Dr. Jörg Althammer; Prof. Dr. Martin Büscher; Niklas Dummer, M.A.; Dr. habil. Michael Ehret; Miriam Fink; Prof. Dr. Manfred Fischedick; Prof. Dr. Nils Goldschmidt; Prof. Dr. Hanns-Stephan Haas; PD Dr. Michaela Haase; Prof. Dr. Ludger Heidbrink; Prof. Dr. Ulrich Hemel; Prof. Dr. Lars Hochmann; Ruzana Liburkina, M.A.; Mark McAdam; Prof. em. Dietmar Mieth; Prof. Dr. Dr. Elmar Nass; Dr. Laura Otto; Prof. Dr. Reinhard Pfriem; Prof. Dr. Ingo Pies; Prof. em. Birger Priddat; Frauke Remmers; Dr. Bastian Ronge; Prof. Dr. Hartmut Rosa; Prof. em. Hermann Sautter; Dr. Philipp Schepelmann; Prof. Dr. Dr. Ulrich Schmidt; Prof. Dr. Markus Scholz; Prof. Dr. Andreas Suchanek; Prof. em. Peter Ulrich
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".