Bibliographic record
Abstract
ast summer, my wife was sitting on campus eating lunch with a friend and colleague when she sustained a brain stem stroke.We are both professors in our early forties and have two daughters, who were seven and three at the time.The stroke came as a complete shock, with no prior conditions or factors that warned of its possibility.In the emergency room, she was diagnosed with a vertebral artery dissection -a tear in the artery's inner layer -that created a blood clot and brain damage, resulting in intense dizziness, nausea, and motor control impairment.Over the first two days, she was in a fugue-like state, drifting in and out of consciousness, and trying to prevent uncontrollable fits of vomiting by limiting movement as much as possible.It was a truly horrible 48 hours.As her condition stabilized, she started to work with the rehabilitation team to regain basic function, such as eating solids, sitting up, standing and walking.She made good progress over the first week and we prepared for a transfer to a rehabilitation centre -plans that were quickly derailed.A week after her initial admission she started experiencing an intense ringing in her ears.This prompted another MRI, which confirmed three additional arterial dissections -all the major blood vessels serving her brain were now structurally compromised, but thankfully (and remarkably), there was no further neurological damage.Further tests were ordered, and further complications were revealed.She was diagnosed with a L The black, white and grey of embracing vulnerability Timothy H. Wideman 2
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".