Bibliographic record
Abstract
[2] subject for the next. If you want a matter of fact one I think this will be just the one for you – “Having eyes to see they see not!” You I am sure can enlarge up- on that grandly; so many pass through this world with eyes en- tirely blind to the grandeur of this earth of ours. Mental blindness also you can speak of, making the mind dark even when there is so much light and happiness, and keen delight for us if we are but willing to let the light in. But you know what I mean and are far better able to treat the subject then I. An [underlined: now] there are but five weeks more till Christmas and I am beginning to wonder what is to be done. I have not yet heard from John, If he should come of course we should all go home [3] and then we could make fur- ther arrangements then, But sup- pose he should not come, what then, would you come? The next term is only two weeks in length and I think I can master sufficient means to carry me through that, as I can live quite cheaply here, but further I cannot see nor do I need, that is far enough. What think you of Father’s [modus operandi?] isn’t it too bad? I am sure I do not know how it is to terminate, certainly Ma does not want to go away out to Canada and yet now that he has bought a house there I suppose she will be very determined. I had a letter from Annie this morning in which she said she was going to teach in the village of [illegible] this winter. I am glad she will have means of her own again, I think it will do her good too, to have a change. I am glad she has had this opportunity.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.835 | 0.733 |
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".