Bibliographic record
Abstract
When I ran track in high school, I always ran the 400 meter dash, also known as the quarter mile. This race was the least favorite of the members of the team, as it was considered the hardest race to run. For the most part, people avoided running this race if they could. The sprinters would prefer to the shorter sprints and long distance runners would stick to the mile and two mile races. I, on the other hand, was intrigued by the quarter mile. It seemed to me like it was a combination of the two extremes, uniting the speed of the sprints and the stamina of the long-distances. I view the short story in a similar fashion. I have an immense amount of respect for the genre as it necessitates the immediacy of poetry and the endurance of the novel. I wrote these short stories the same way I ran the quarter mile: as hard and fast as I could for as long as was deemed necessary. And along the way, I stuck with a couple of rules. 1) Hook the reader right away. Any self respecting reader should put the story down if he or she is not interested after the first page. 2) Don't lecture your audience: a good writer raises questions, not answers them. Like Chekov said, what is "obligatory for the artist [is not] solving a problem, [but] stating a problem correctly." 3) To tell the audience more than they need to know is condescending. Overcompensation can kill a story. 4) Lastly, be as honest as you can about the human condition. Don't fake a situation for narrative sake. If I stuck to these rules, I generally found I'd end up with stories about interesting people in intriguing circumstances. Nothing more, nothing less. More than anything, I wanted to be like the successful quarter mile runner. To keep a stead pace throughout the race. To finish the race before flaming out. To raise my arms in glory knowing I'd conquered the hardest of engagements.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.128 | 0.034 |
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".