Bibliographic record
Abstract
In this article art is used as inquiry to ask powerful questions, untangle paradoxes, and help us navigate loss and grief in the Anthropocene. Several central questions are considered and animated through narrative and poetry. How do we live poetically (Leggo, 2005) in a world that we need to exploit in order to survive? How do we engage in a more-than-human world full of ambiguity and paradox? How might nature become a teacher or mentor (Jickling et al., 2018), and what anthropocentric barriers do we face? How can stories and poems facilitate holistic expression and place-based connection? As we elucidate the wonder and loss of cottonwood, and the mentorship of ponderosa, Carl Leggo (2004, 2005, 2012, 2016, 2019a, 2019b) serves as a guide for artful attending and hopeful imagination for living poetically. Joanna Macy’s (Macy & Johnstone, 2012; Macy & Brown, 2014) work that reconnects and Leggo’s curriculum of joy offer parallel paths of grief and hope so that we might find our way through the Anthropocene.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 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; both teacher heads agree on what is shown here.
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".