Bibliographic record
Abstract
In October 2022, A/P Justin Leontini (Dept. of Mechanical and Product Design Engineering) and A/P Max Schleser (Dept. of Film, Games and Animation) ran a mobile storytelling workshop with a regional primary school in Victoria, Australia. Pupils learned digital storytelling and rookie smartphone filmmaking basics to engage in a discussion about energy. The co-created (Auguiste 2020) mobile stories are part of the development of an Energy Literacy Community Toolkit, which is developed by Schleser, Wheeler, Leontini and the Social Innovation Research Institute at Swinburne University of Technology for Central Victorian Greenhouse Alliance in connection with the Donald & Tarnagulla Microgrid Feasibility & Demonstration study. This study is supported by Centre for New Energy Technologies (C4NET) and the Department of Industry, Science, Energy and Resources (CVGA 2022) in Victoria, Australia. This research creation project Where does Power come from? illustrates the Community Engagement: Donald and Tarnagulla Microgrid Feasibility Study project’s process and the co-created understanding for the next generation of power end-users. The research objective was to develop a community voice in the microgrid feasibility study. The co-created mobile stories are developed with an interest-based model (McCosker et al. 2018). Through understanding the communities’ perspectives and perceptions, this research worked with micro, horizontal hierarchies and ‘open space’ storytelling approaches (Zimmermann and De Michiel 2018, 2, 33 and 31). The theoretical underpinning draws upon a legacy of community arts (Dunn and Leeson 1997) and activism notion of ‘nothing about us without us’ (Charlton 2000). The co-created method enables to develop an understanding about particular thematic through applying a community point of view or language to develop a resource for the community. Mobile story making (Schleser 2018) is a conversation starter. The pupil’s, their parents, neighbours and other community members will ‘like’ these videos because they are featured in these stories (Gondry 2008). The project shifts the conversation to the heart of the community and provides new community engagement touch points. Understanding local community needs such as reliability, cost and self-sufficiency is a key element in setting up a community microgrid. Rural and regional communities tend to be at the end of the grid or have long distribution lines, leading to quality deterioration of the electricity supply. These communities and their businesses are seeking a balance of reliable, sustainable and low-cost electricity tailored to their needs (C4Net 2022).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".