Sens sylvestres : désapprendre, ressentir et incorporer la forêt Tarkine (Tasmanie, Australie)
Bibliographic record
Abstract
Questioning animals, forests and more broadly nature as we question humans in anthropology: here was the intuition that I decided to follow, which led me from the confines of the Yukon (Canada) to Tasmania (Australia). What happens when you immerse yourself, for weeks or months, in the heart of a rainforest, nestled in the northwest of Tasmania? First of all, we feel lots of things. Threads, which we gradually learn. Textures that we can tame. And smells, which become familiar over time. Time. Let's talk about time: does it exist in the present of the forest? To learn the forest, I had to start by unlearning it; or rather unlearn the numerous constructions with which I tried, clumsily, to understand it. But we cannot unlearn to become a blank slate. So I chose to feel. Feel to letting these sounds, smells and textures invade my body. To let my body learn and know, in turn. To let contact with this forest not only affect, but transform my whole being to the world. And to formulate these sensations, these transformations, these learnings, inevitably transforming them in turn, in order to be shared and discussed. Each step of the process is described, justified and questioned. There are so many questions. And to answer it, comes time to welcome. Welcome the experience of non-linear temporality. Folded temporalities that unfold to those who are interested closely enough, to those who listen beyond words. Welcoming the sensations of these continuities where bodies and subjectivities are entangled in these unfolded temporalities, which are embodied in space and in these variations between life and death. It is a proposition of this world, of these learnings, of these questions and of this welcoming which offers this text, an invitation to listen in order to feel, to decenter oneself in order to welcome.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".