How is Canada’s energy future projected? A case study for synthesis mapping in net-zero energy modelling
Bibliographic record
Abstract
Synthesis mapping is a tool used to visually communicate evidence and expertise of a multi-layered system. It distinguishes itself from other forms of systems mapping with the use of visual metaphors, an iterative approach, and the end goal of enabling stakeholder discourse. The work of Jones et al. (2017) provides an exemplary demonstration of this technique. Energy system modelling, on the other hand, is a largely computational approach. Models typically use depictions that are calculative formulas or workflow diagrams for use by other experts in the field and are often stand-alone. Arguably, energy system modelling itself is a multi-layered system. These layers consider the use of energy across sectors, what energy is available, and impactful trends from other domains of expertise. While not a social system itself, a more common application for synthesis maps, energy system modelling can have some impact on social systems. This is particularly true if used by policy analysts in the shaping of government policies. As Canadians and policymakers increasingly look to modellers for insights on complex questions about energy, energy system modelling is facing new interest in its method from stakeholders who may not be modellers themselves. As such, synthesis mapping presents a unique opportunity to share this knowledge. This paper explores one such opportunity in the communication of the Energy Futures Modeling System. Developed and used by the Canada Energy Regulator to produce yearly energy projections, this energy modelling system is newly pictured in an accessible format on a publicly available website. This paper demonstrates a process for synthesis mapping in this unique context and proposes its application to new contexts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".