Constructing Collaborative Online Communities for Visualizing Spimes.
Bibliographic record
Abstract
The digital age has brought about new platforms for collaboration which have provided interesting and effective ways of enabling people to engage in a wide variety of socially-driven activities. One only needs to observe the many free/libre open source software projects on the web, where millions of connected individuals actively participate in the development and deployment of a wide range of software applications and tools. For many of us, there is a great appeal to this ideology, one comprising of a more transparent and open culture of collaboration. Such activities encourage freedom and shared learning which could be considered essential to human growth and innovation. In this paper we describe research with such goals. Specific to our research includes the development of online and mobile user interfaces for the visualization of food ``spimes'' (informationally-rich food-based data), seeking to understand how best to enable and encourage people to share information/knowledge, visualize/compare choices, and understand different aspects of food quality. By democratizing food knowledge in such respects, it is the goal that we develop a more satisfying food culture, enabling people to collectively realize more healthy, socially acceptable, environmentally friendly, and cost-effective food choices.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".