Canada’s Net Zero -440 Megatons of CO2 by 2030: Is a battle between Human System Dynamics and the Political - Economic systems.
Bibliographic record
Abstract
Canada enjoys a natural resource based economy and therefore a tacit beneficiary signifying the carbon embedded “hockey stick of economic growth” from its’ wealth of fossil fuel and mining industries. However, affected by climate change, Canada is determined to mitigate its carbon emissions and thus committed to reduce CO2 emissions by at least 40 per cent below 2005 levels by the end of the decade or to no more than 440 Mt a year in 2030 en route to net zero by 2050. The research question: What could be the most productive and objective financial incentive program that must be implemented to avoid policy resistance by the general public and, if those incentives potentially spur equitable and tangible returns to citizens and transformation outcomes for Canada’s political -economy and the natural environment. The research intends to answer this question by undertaking a Qualitative primary research via a mailed out survey. A purposive sample of randomly selected households voluntarily respond to the Likert scale rated Questionnaire informing the economic independence of business organization, the inter-dependence of human system dynamics with the natural environment and the impact of political -economic policy mechanisms on society and business. Researcher’s bias in designing the questionnaire has been considerably eliminated by requesting the respondents to provide their own comments in writing in a separate section. Insights from the research reveal an evolving socio-economic dimension augmented by technology advancements. It further emphasized the human relationship to energy efficiency through Affordability, Accessibility, Acceptance and Acquisition and that climate accountability can’t be to increase top line business revenue, rather to develop an equitable and sustainable Green economy for the people and nation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".