Bibliographic record
Abstract
Research Article| August 01 2023 Lessons from a Kangaroo Kelly Donati Kelly Donati Kelly Donati is senior lecturer in food systems and gastronomy at William Angliss Institute (Melbourne). Her research explores multispecies encounters in food and farming practices of the Anthropocene. She is a founding director of Sustain: the Australian Food Network, a not-for-profit focused on research and policy for food system transformation. Kelly.Donati@angliss.edu.au Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2023) 23 (3): 1–6. https://doi.org/10.1525/gfc.2023.23.3.1 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Kelly Donati; Lessons from a Kangaroo. Gastronomica 1 August 2023; 23 (3): 1–6. doi: https://doi.org/10.1525/gfc.2023.23.3.1 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentGastronomica Search Great economizers of energy, kangaroos travel long distances with ease and efficiency. Their unique locomotion symbolizes movement and progress on the Australian coat of arms, capturing the spirit of a young, forward-looking nation in the colonial imaginary. Emblazoned in red across the tail of Qantas airplanes, Australia's largest airline, a bounding kangaroo in full flight signals the exciting possibilities of effortless travel. In my own anthropocentric fantasies, I have always felt there is something very human about how kangaroos appear to revel in the capacities their clever physiology affords them. Decades after moving to Australia as an adult, spotting a mob of kangaroos propped on their elbows in casual recline as they laze in an open paddock still sparks childlike excitement. On my visits back home to Québec, my niece and nephew would press me for stories of these strange, charismatic creatures from the other side of the world. In... You do not currently have access to this content.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".