Life in 2030
Bibliographic record
Abstract
Life in 2030 is a ground-breaking, practical, and, above all, positive vision of life in twenty-first-century Canada. As we move into the next century, the development of sustainable and environmentally benign patterns of resource utilization and socioeconomic development is an essential priority. In this book, John Robinson and his co-authors investigate the possibility and impacts of a sustainable future for Canada. Based on research initiated by the Sustainable Society Project in 1988, Life in 2030 is unique in that it uses backcasting instead of forecasting to trace the path of Canada forty years into the future to the year 2030. Instead of predicting the most likely future based on current trends, the authors set out a desirable future and discuss the changes that would need to occur between 1990 and 2030 to arrive at this future vision. This vision, derived from ethical, political, and ecological principles, is not viewed as definitive, for the authors hope to inspire others to conceive of, and work towards, their own visions of a sustainable future. Life in 2030 makes a significant contribution to interdisciplinary studies on the environment and sustainability because it develops a scenario that allows for an evaluation of the changes required to achieve a sustainable society. This book is required reading for anyone interested in a sustainable future environment. It also provides an original and provocative look at life in Canada in the twenty-first century.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".