Implementation of a Participatory Design Approach to the Development of a Sustainability Decision Support Tool for Canadian Egg Farmers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The National Environmental Sustainability and Technology Tool (NESTT) is an online sustainability assessment and decision support tool developed for Canadian egg farmers in two phases—Lite NESTT and Full NESTT. To ensure that users (egg farmers) have a say in its design and development, and to foster a sense of ownership of the tool, a participatory design process was implemented in the development of NESTT. Specifically, a four-step participatory design process was adopted for this study with two discovery phases. The pre-launch discovery survey diagnosing use situations resulted in Lite NESTT being focused primarily on resource use efficiency and productivity, prioritization of benchmarking, and defining the focus areas for the prototyping phase. In the prototyping phase, farmers were interviewed with renderings and mock-ups, and improvements related to user-centeredness, data security, aesthetic appeal, accessibility, and simplicity were achieved. Finally, the post-launch discovery phase helped in defining the new features for Full NESTT such as the implementation of carbon footprint assessments, information on funding opportunities, and fixing data input issues. This last phase also helped in identifying several long-term strategic options to consider for NESTT such as integration with other on-farm programs, integrating economic assessments and financial incentives into NESTT, and adding more customized, farm-level decision support features.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it