Bibliographic record
Abstract
In this explorative essay, I discuss the complexity of apparentness as it pertains to being a teacher educator and an artist. Notions from creative and postmodern research approaches help me suggest that it is a quality of perception and not a quality of art or any other kind of text. In this exploration of how apparentness comes in and out of focus for me as an educator and as an artist, I begin with a discussion of the role of art in my becoming a teacher educator; I realize art’s efficacy as a site for exploring the acts of facilitating discussions, drawing the attention of others, and raising an awareness for what we believe matters. As a painter, I reflect on and inquire into what is laudable and limited in these forays of connecting art to teaching and education. The research approach is narrative and reflective, not intent on the discovery of discreet findings, but more interested in discovering questions and in offering some ways of framing my experiences and their possible implications for educators, teacher education, art, and artists. I ponder the parallels between art and education, particularly in how viewing art can be a metaphor for classroom discussions. The essay is concerned with what we believe is apparent and mysterious, and how we engage in dialogue with the resonance and dissonance of the perceptions of others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.009 | 0.075 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".