Bibliographic record
Abstract
Watching Deep Space Nine was an exercise in peering around corners and being unable to get a straight-on view of what I knew to be just beyond my field of vision. Not necessarily in terms of the greater world of the Star Trek universe – that I could see much more easily, as DS9 is still the only Star Trek series that took the time to consider what day-to-day life would be like in the greater world of the show outside of Federation starships thanks to it being set on a space station, boldly parking instead of going – but very specifically, in regards to Julian Bashir. The show’s primary medical character, a bright young doctor straight out of Starfleet Academy, who quickly learns there’s more to his mission than he thought while never losing any of his dedication or kindness. I’ve read about Alexander Siddig’s portrayal of Bashir, of his work turning him from someone deliberately unlikable into one of Star Trek’s beloved characters. I’m familiar with Bashir’s backstory and growth, his canonical developmental disorder and his transformation from fresh-faced graduate to hardened, mature officer. And throughout it all, I’ve always wondered, did they mean for him to sound like me?
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| 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".