Hacking the Techno-Transition: The Possibilities of Deep Energy Literacy
Bibliographic record
Abstract
This article takes the E.L. Smith Solar Farm at the E.L. Smith Water treatment plant in Alberta – a province at the epicentre of Canada’s oil and gas industry – as a case study for what I call deep energy literacy. An energy transition away from fossil fuels to sustainable energy sources is a necessary first response to climate change. Deep energy literacy is a proposition, a set of theoretical concepts, through which to disrupt, or “hack”, technophilic transitions by attending to intersectional feminist and decolonial politics and solidarities. Technocratic solutions for decarbonization that do not radically reorient existing social, economic, and political relationships are failed solutions even before implementation begins because they have not addressed the root cause of climate change: a bankrupt extractivist worldview. This worldview is the cause of not only climate change but multiple converging crises. Deep energy literacy is a proposition grounded in relationality that can help us identify problems more holistically and thereby come up with solutions that not only address necessary energy transition shifts, but that do so while simultaneously addressing a plethora of other concerns – including but not limited to Indigenous (re)conciliation – by creating more equitable and just societies and ecosystems. Seen through the lens of deep energy literacy, this analysis of the processes through which the E.L. Smith Solar Farm project was approved illustrates that when decisions about new energy infrastructure are based in entrenched economic, political, social, and epistemological paradigms, they fail to disrupt the status quo and therefore fail to adequately address the root causes of climate change. To achieve a just transition many experiments need to take place; many of these experimentations will be imperfect. In the case study considered in this paper, I suggest that while deep energy literacy conversations were begun, they were not integrated fulsomely enough. Nonetheless, there are positive lessons to be taken from the E.L. Smith Solar Farm and integrated into future decision-making processes.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.081 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".