Bibliographic record
Abstract
Can the two narrative autoethnographic stories I perform here help me to therapeutically deal with the losses they describe? The first story speaks of my PhD defense and its aftermath. I consider the event a loss that is clothed in accomplishment; I work through my feelings by writing about them. The second story describes a medical loss. After attaining the PhD degree I finally acknowledge that I have Dystonia, placing me in the unfamiliar world of the chronically ill. I use therapeutic autoethnography to reflect on both stories, and I ruminate over how the stories are entwined but very different. They have each (re)shaped my identity; I am in the never-ending process of (re)storying my self. Although the artifacts garnered from living these stories may be construed as reminders of loss, I have found a way to minimize the two losses by working with both artifacts together.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".