Building the bioethics tools of a community council to the future: the ecosystemic gap
Bibliographic record
Abstract
Abstract These are times of crisis. Recently, the COVID-19 pandemic and the resurgence of a form of Cold War raised international concerns about Health & Well-Being, Climate & Biodiversity, and Technology & Economy. Articulating bridges between disciplines, between cultures and between knowledges has never been more urgent to accelerate the translation of values and policies into actions. This comprehensive review argues for a radical ecosystemic approach to bridge the Medical & Environmental fields (studies, sectors, and technics) in an integrated management practice of Care, Production & Biodiversity. As bridging implies solving the epistemological gap, the argument emphasizes the need to raise awareness with theoretical hybridizations, fieldwork hypotheses, and working theories. According to Van Rensselaer Potter, who coined the term ‘bioethics’, awareness means to refocus the Medical & Environmental studies and surveillance processes from a target (e.g., the disease, the pathogen, or the resource) to its context (e.g., adding history, demography and ecology). Thus reframed, concerned researchers, leaders, and citizens should invest their effort in preparing the (contextual) terrain for ever-more organizational resilience. We conclude on the need for actions to shape the Health & Biodiversity determinants, to improve communication systems, data-sharing networks, and responsible innovations, and to foster knowledge translation to envision a better realistic future. “Ecology’s uneconomic, but with another kind of logic economy’s unecologic” (Potter 1988, p.9)
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 | 0.131 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.017 | 0.024 |
| 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".