Bibliographic record
Abstract
I am guilty of allowing myself to become distracted by issues about which I can do very little. The foremost of these issues is practical: the inability to make much money as a Canadian short story writer. Tied directly to this is the extraordinary amount of quiet time I need in order to write anything which holds my interest. By quiet time, I mean the kind of time where, for days and nights running, the world of the imagination takes hold. I would not want (would be afraid of) a life filled with this kind of time, but I do need substantial chunks of it to get to the place where I can make a piece of fiction. All of this is further complicated by a middle class determination to raise my children well (to accommodate their potential, creative and other) and to live in something other than squalor. In short, I want it all. I want enough money to service my middle class family needs and to free my imagination to write, but I need a full time job to get it; a full time job precludes writing. If I were young and beautiful I would marry for money. If I could water down my own puritanical literary code, I would write potboilers full of greed, lust and violence to subsidize the work I care about. The romantic concept of artistic struggle is pretty much lost on 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.107 | 0.080 |
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".