Bibliographic record
Abstract
Ballads for Re-Membering is an examination of themes of consciousness such as\ntime, space and emergence, set against the precarity of the climate crisis. Using an\narts-and-Zen-practice-based methodology of research-creation, and a theoretical\nframework of New Materialism and Post-Humanism (“K(now)n Materialism”), this\nfeminist response to the climate crisis manifests an otherwise-possible that is already right-here, taking cues from the emergent playful worlds of childhood studies and music, with interbeing as the net holding all.\n\nThe dissertation is important because the climate crisis is one of (if not the most)\npressing and crucial challenges of the present, and we must keep finding ways\nto address it using our imaginations. Because my work uses a research-creation\nprocess, the methodology provides a unique opportunity to look at a complex set of\nissues in a nuanced and artistic way. The form also belies the function: These creative\nand academic outputs fold and knead together conceptual spaces; playful and\nengaging imaginings that slip through our habitual systematic thinking in linear\ntime, space, and forward progress.\n\nIn the form of three Ballads, I look at time, space, and emergence (Ballad 1), offer a\ncritical analysis of the New Nature Movement, look at childhood and time, and music\n(Ballad 2), and perform an audio story called Finding Solace (Ballad 3). This young\nadult story takes the research to the speculative: What if there was a world (mostly)\nwithout humans, where Artificial Intelligence was so intelligent, it went on “living”\nwithout humanity?\n\nThe key results of this work include making new forms of knowledge as ways of\nunderstanding our precarity; innovative research methods like song, story, and letters\nto my daughter; engaging and accessible research outcomes, and contributions to\nthe field of New Materialism, Research-Creation, and artistic responses to climate\nchange. It is my hope that this work encourages other researchers to explore\ninterdisciplinary approaches, allows for engagement due to its accessibility, and\ncontributes to a greater understanding of our interbeing.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.199 | 0.109 |
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".