Bibliographic record
Abstract
Give every man thy ear, but few thy voice.Take each man's censure, but reserve thy judgment Costly thy habit as thy purse can buy.But not express'd in fancy, rich not gaudy.For the apparel oft proclaims the man.And they in France of the best rank and station Are of a most select and generous chief in that.Neither a borrower nor a lender be.For loan oft loses both itself and friend.And borrowing dulleth th' edge of husbandry.This above all: to thine own self be true.And it must follow, as the night the day.Thou canst not then be false to any man. (1.3.59-80)T he modem tendency is to find these precepts unimpressive.Harry Levin laughs at them as "etiquette rather than ethics" -and productions of the play will often help us laugh by making Laertes fidget while his father recites them.One production, earlier this century, had Polonius pull a little book out of his pocket and read the precepts from it, demonstrating by stage business that they are not to be "charactered" in memory, since even the man who preaches them does not have them charactered in his.But this matter of "charactering" in the memory is a good bit more serious than
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".