Off the beaten track: a reflection on intention and unpredictability in arts education research
Bibliographic record
Abstract
The coherence of a 'research program' is often betrayed by the unanticipated turns and detours in arts research.The following article reflects upon the place of the unexpected in arts research, the alternative ways in which knowledge or 'findings' are often constructed, and the complexity of calibrating or measuring arts research for broader publics.UNESCO's roadmap is seen here as a site for further deliberation, a point in time and space that should engage arts communities in rousing dialogue-locally and globally-about the convergences and divergences of our practices and research paradigms. A Roadmap of SortsAcademics like to talk about a 'research program' as though our research work is always a coherent proposition, a linear program with obvious beginnings and predictable outcomes.I would venture a guess that many arts researchers began their careers as teachers of the arts, or artists, or both.And these auspicious beginnings are likely what drew them to research in the arts.So when I think now about "navigating the UNESCO roadmap for arts education" as the special issue of this journal promises to do, I look back on my last 20 years as an arts teacher and researcher and realize that in this eclectic mix of experiences, I have been involved in a process of imagining art as an entry point to a life examined.The arts have been the navigational tools that have, for me, raised questions of great importance in the field of education broadly speaking.A 'roadmap' in research is a very useful concept.Roxana Ng and Kiran Mirchandani (2008) have recently used the concept of "mapping" as a conceptual and methodological tool to link lived experiences with institutional processes.This, too, is a useful idea for arts education research.How do we understand the relationship between arts educators and the institutions in which they work?These researchers use mapping as
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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 | 0.228 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.044 | 0.214 |
| Scholarly communication | 0.043 | 0.049 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.025 | 0.082 |
| Insufficient payload (model declined to judge) | 0.002 | 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".