Bibliographic record
Abstract
If a week goes by and I am not asked "why do you write?" I feel something is missing. It is a question that has become as much a part of what I carry in my big purse as my wallet, makeup, pens in every colour along with the old fashioned notebook made of paper. When I first started writing in my Hilroy notebooks with the thick blue lines I was nine years old and fresh "off the boat" as they used to say, although that boat had been an enormous ship named the Augustus that had sailed over the Atlantic for thirteen days with a cargo of bewildered mothers, timorous children, young men and women reeking with expectation as they made the journey to the land of maple and honey. I am now six times nine and the memory of the voyage is as clear as if it had taken place yesterday.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.061 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".