Combining Design Thinking and the Socio-Technical-Ecological Systems Perspective to Understand Greenhouse Growers’ Experiences with Energy Management Solutions
Bibliographic record
Abstract
Multiple threats to sustainability are driving the need to grow food in controlled environments, such as greenhouses. However, greenhouses consume large quantities of energy for lighting, heating, and ventilation, which places additional strain on the natural environment. For both business and environmental benefits, greenhouses must pursue sustainable energy management solutions. Combining design thinking with the socio-technical-ecological systems (STES) perspective, we analyze the greenhouse grower’s journey from awareness of potential solutions to post-implementation use. Our approach offers a novel way to understand the problem space. We find that sustainable energy management is more than a technical or even socio-technical challenge; it also involves important ecological considerations. However, ecological and social concerns are less evident in the grower’s journey as compared to the physical and information technology dimensions. The research and development of sustainable technology solutions would benefit from giving equal attention to these three systems and the interactions between them.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".