Bibliographic record
Abstract
Someone once said, tell me where you're from and I'll tell you who you are.The environment we live in, both built and natural, frames and forms us.This issue of the Journal of Cultural Research in Art Education is focused on the constructed environment, both in its physical manifestations and in the environment we construct and inhabit in our hearts and minds.The natural environment is becoming a smaller and smaller part of our world ( does any environment exist that's not in some pait formed by humans?), and the built environment looms ever larger.And as we construct it, so it constructs us.Terraced farm plots on hillsides; brownstones and subways; ranch-style homes with chem-green lawns; cobblestone streets, courtyards and plazas; corrugated steel walls and snow drifts: the environments that frame and define us as Spanish or British Columbian or Inuit or proud citizens of Eugene, Oregon, or Tribeca, provide the big context for our character.The things we bump into in those contexts help make us into baseball players or accountants or art educators.Whether consciously or not, we shape the environment with our values, and in turn, our environment shapes our values and the values of our children and our children's children.It is my hope that by making conscious the values that underlie and imbue our constructed environments, through critical aesthetic inquiry, we can gain insights into developing and maintaining healthy environments that sustain us body and soul.Toward that end the articles in this issue of the Journal of Cultural Research in Art Education focus of the socially embedded aesthetics of the places we live-both physical and social.B. Stephen Carpenter addresses Pat's Barber Shop, the place where he not coincidentally gets his own hair cut, as an example of hypertext that framed properly is an educational environment that offers a lot of insight art education's concerns in an era of visual culture.Lisa Waxman focuses on third places, in the form of coffee shops where everyone-well some people, anyway-know your name.Kristin Congdon, Steve Teicher, and Adrienne Engell address the use of technologies to portray local heritage on a bus system.Mary Stokrocki and Mariusz Samoraj describe and analyze a Polish "green school" experience.Debrah Sickler-Voigt reports on teaching and learning centered around so-called at risk kids mentored by self taught artist 0. L. Samuals.Michelle Kraft addresses equality and inclusion for students with special needs in terms of creating a communitarian environment.Jack Richardson takes on the structures of art education as rigidified community values through the lens of
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".