Toward Resilient, Inclusive and Vital Technological Infrastructures for the Energies of the Landscape
Bibliographic record
Abstract
Today, the implementation of energy infrastructure needs a complex â"dialogue" between two apparently different aspects: technical aspects that characterize the processes of production and transformation of energy for the territory, and socio-ecological aspects related to the biological, organizational, and economic variables for cultural, creative, and productive energies of the territory. This new dimension of designing and building the energy infrastructures replaces to the classic esthetical idea of the landscape an integrated vision of human habitat in which innovations play a key role for the redefinition of relationships between téchne, bios and oikos. On these subjects, this article proposes a reflection on a methodological approach to re-think energy infrastructures as technological-environmental interfaces between land resources, energy needs, living dynamics, and inhabiting practices of territories and cities. Four possible scenarios are presented starting from some research experiences developed on Italian Abruzzo region. Through these experiences, emerges a new strategic, tactic and operational framework for the design of energy infrastructures. A new design framework in which energy infrastructures can contribute to re-build relations and connections between the acceptance/correlation of technological innovations and resilience, inclusiveness and vitality of the landscape.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".