Bibliographic record
Abstract
This narrative essay unfolds in two parts. Part 1—Feeling My Way into Embodied Research—reflects on my doctoral fieldwork, which involved living for a year in complete solitude in the remote wilderness of southern Chile. The life events that shaped this experience, including childhood, education, early career, and graduate studies, laid the physical, emotional, intellectual and spiritual groundwork for the project. A key challenge was to develop a methodology appropriate to the unusual context of my study. My methodology was grounded in mindful observation layered with analytic introspection, loosely based on autoethnography. Part 2 Feeling My Way into Embodied Writing—recounts the process of transforming my 900 page wilderness journal into a doctoral dissertation that was both academically rigorous and accessible to audiences. I abandoned the traditional academic approach of using a conceptual framework to organize the work in favour of a first person narrative, punctuated with analytic essays. Rather than write a dissertation about solitude, I let the voices of solitude speak directly to readers and evoke for them the actual experience as I had lived it.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".