Global Energy Transitions: Coming to Terms with Inter-generational Burdens
Bibliographic record
Abstract
Coming to Terms with Inter-generational Burdens The need to reduce greenhouse gas emissions to address the challenge of climate change is an issue of intergenerational burdens that requires a cogent national strategy for managing a future liability. We propose a fresh approach to move away from the constraints imposed under the UNFCCC1 process requiring an enforceable multi- lateral agreement amongst all nations- a treaty with targets and timelines. The two decade UNFCCC process has not delivered meaningful results and in essence become a curse- the T3 curse. We propose a strategy for Canada to lead by example. The goal is to provide long term policy stability and a coherent framework for action within a national context. Such an approach can be readily adopted by other jurisdictions. The essential components of the plan include: i) A ‘cap and invest ’ strategy with a long view: large investments for accelerated deployment of low carbon technologies on a scale that enhances the national scientific, technological and industrial capacity ii) A small levy on economy-wide consumption: one percent of current consumption. This levy would generate a large pool of capital for investments on an on-going basis to de-carbonize 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".