Bibliographic record
Abstract
ABSTRACT “Swamped” cracks open my experience of depression by exploring how a specific place—a swamp—acted on me to bring social and emotional injuries, but also modes of seeing that ultimately moved me out of the depression, to the fore. In writing from this specific place, I build on moments in which something—a desire for beauty, the luminosity of blue, the dullness of gray, the vibrancy of lichen, and the slippage between seeing and unseeing—moved into view. These moments were often minute and small and could seem as if nothing had happened, but in each one something impinged, and something congealed released itself into vision and movement. In placing these moments in loose sequence, I do not only seek to clarify how and why vision matters to me but also to form a method for seeing from which I might be able to draw should the depression strike again. In depression, rational understanding often runs out, pushing memories and associations to the fore. The creation of this piece has been inspired by the associative‐autobiographical writing developed by Maggie Nelson and Wayne Koestenbaum, as well as the image‐bound, descriptive approach encouraged by Kathleen Stewart.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".