Cultivating Research Through Digital Ecosystems
Bibliographic record
Abstract
Abstract The research culture of art education is an ecosystem of ideas and inquiry. This ecosystem of research extends into the varied forms of digital mediation. Now in its fourth year, the National Art Education Association Research Commission’s objective is to cultivate, connect, and amplify art education research. In this essay, we theorize the analogy of research ecosystems and use the example of our Interactive Café as a space that fosters research culture. Digital forums such as the Interactive Café function as a place where individuals who produce and use research can interact and exchange ideas. Our position is that digital mediation needs to strengthen interdependence and vibrancy through spaces and events that connect a diversity of knowledge producers and stakeholders. For the Research Commission, research that is born digital is ripe with potential to connect, evolve, and amplify throughout the field.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".