Parallel Session 4: Friday 15 November, 13:30-15:00
Bibliographic record
Abstract
This workshop will present and discuss future societal needs by identifying links between environmental, social, health and economic factors. It will discuss the principles of sustainable development and in particular how it links with public health and wellbeing for all. We have to change our lifestyles quite drastically in order to remain within the planetary limits of our natural resources. How can we do that? How can we make our lifestyles more sustainable and healthy at the same time and create opportunities for everyone? This workshop will identify enablers and barriers for more sustainable societies such as related to policy and governance, the economy, environment, to social and technological innovation, behaviour change and wider social determinants of health equity. The workshop will demonstrate that there are positive trends and solutions possible, for example linking health and nature, but new research is needed to capture these innovations and to investigate how they can be scaled up or mainstreamed. Public Health researchers, professionals and decision makers should work more closely together with sustainable development stakeholders and other disciplines and include health equity impact assessments. The workshop will present the plans of EuroHealthNet for a new European Centre for Innovation, Research and implementation for Health and Wellbeing that aims to look ahead and anticipate how societies might develop in the future. A discussion will be facilitated on what research is needed to inform policy to respond to new trends such as demographic change, urbanisation, climate change and the environment, economic issues, technological and social innovation, in anticipation of the new Horizon 2020 programme.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".