Bibliographic record
Abstract
Through this paper, the author tries to explore a simple yet complex question: how do we decentralize the human presence in conversations about climate-change? To do so, this speculative climate 2ction is presented through the non-human narrative perspective of mycelium (fungi). The speculative fiction provides a space for re-thinking our ontological and epistemological strategies and categorizations of nature/culture division, as well as how we understand nature in relation to human.The speculative climate-fiction proposes a reconsideration of human in relation to nature/climate, through fungi. It further explores how sensory, bodily, and multimodal methodologies may work in interaction to produce new possibilities to explore the corporealities of human-nature relationships and how a non-anthropocentric understanding of climate-change can allow for an emerging engagement with a vast mesh of human and beyond-human agencies. Drawing inspiration from Sylvia Plath, Ursula K. Le Guin, Margaret Atwood, and using Erin Manning’s understanding of a5ect as having a feltness that we often experience as a becoming-with, in this case, a becoming-with nature, the speculative-fiction (SF) is written as a dialogue between fungi and human. The SF also uses artwork created with mushrooms, fungal roots, as well as mushroom extracts, to exaggerate the presence of beyond-human beings in a new onto-epistemic strategy that reconsiders climate change and human–nature relationships.
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 | 0.001 | 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 | 0.005 | 0.026 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".