Bibliographic record
Abstract
There forms around human mind and senses a rust, a patina, a moondust layer of indifference, a despair at ordinariness of world we live This has to be brushed off, chipped away, live skin of present sensation and thought left open to past and future, old songs must be heard and new ones sung or dance will die and spirits will be no longer honoured. To change metaphor, it has seemed to me that Old English Riddles—those in Exeter Book, for instance—are like glassbottomed boats that let us see, drifting just below us in depths of everyday things and common beings, creatures incredible yet real, in colours and light unbelievable yet visible, so that we marvel once more at how human creature lives in this shallow ocean of air, just above deep ocean in which ancestors once breathed, the mind, that ocean where each kind / doth straight its own resemblance find, as poet Marvell said. I have tried to revive riddle-form by looking at mysterious inwardness of ordinary things here and now, in our own world, that is, here in North America in twentieth and twenty-first centuries, as computers count them at present time. In this riddle-form, some ordinary created beings speak to reveal some of their mysteries, let some of their powers up from their depths into the boat we all are in. Here is what a house says:
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".