Challenges for science and society in an era of rapid environmental change – a role for the NSIS?
Bibliographic record
Abstract
EDITORIALChallenges for science and society in an era of rapid environmental changea role for the NSIS?This Issue of the PNSIS has contributions on a wide range of topics, from marine chemistry to ecological research in wilderness areas of Nova Scotia.The commentary on ocean health is especially noteworthy, as it brings attention to the region's ocean science sector, and the vital connection between ocean and human health.The papers in the PNSIS and our monthly NSIS talks illustrate the wide range of local scientific expertise and output and how it contributes to our society's knowledge, well-being and productivity.Science has been the key to the human and social enterprise of the 19th and 20th centuries (Dubos 1970).It has shaped the modern world at a time of a major population increase, brought on by better living conditions and medical care, vaccines and the increase in available food.At the same time, there have been continued human tragedies as a result of wars, pandemics, mass migrations, and starvation related to social unrest and localized food shortages.Despite such challenges, much progress to improve the human condition since WWII has been facilitated by the United Nations and its many front-line agencies.On this theme, it is well worth reflecting back 50 years to the pivotal conference on the environment, held in Stockholm, Sweden -the 1972 United Nations Conference on the Human Environment (Ward and Dubos 1972).It was the first of its kind to discuss the environment from a global humanities perspective.It came as a result of the waves of public concern about the environment that began in the 1960s.This was spurred on by the over-riding threat of nuclear war, an awareness of the spread of persistent toxic chemicals (Carson 1962), and as already mentioned, concerns about the rapidly increasing human population, at that time being 3.5 billion (Ehrlich 1968, Regier and Falls 1969, Meadows et al. 1972, amongst others) and now close to reaching an alarming 8 billion.At the Stockholm conference, in addition to important discussions of the above global concerns, it was already noted that rising CO 2 levels from man's activities could lead to a significant rise in the
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".