Social Shopping Using Food Spimes
Bibliographic record
Abstract
Bruce Sterling defines spimes, in part, as extensively rich streams of data and informationabout things. From a theoretical viewpoint, the concept of spimes is indeed interesting,with seemingly endless possibilities for enriching our knowledge about the things all around us.In terms of our everyday decision-making activities, spimes could have significant influence onour behaviours, empowering us to make more informed choices. No where is this more true thanin topics relating to sustainability, especially in how sustainability relates to the selection of thefood that we eat. With vast amounts of information available, the issue of selecting good food canbe difficult and more adequate support is needed. This paper proposes a framework for designby discussing a model of social interaction which encourages, engages, and motivates consumerparticipation, enabling consumers to share experiences and bridge knowledge barriers. By developinga framework for community support in such respects, we ensure information quality,transparency, and potentially provide more effective consumer support accordingly. Thus, wehave a greater chance of choosing better food selections, specifically those from the perspectiveof integrating more sustainable choices in our everyday food selections.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".