Bibliographic record
Abstract
Letttr furfiJ in i" «-» --'rtiftr tf September », 1769, AI. 10869, from ivbftKi it ivas ctpifd into mil tbt Paw, and bearing Datt, Plymouth, Auguft u, SIR, * OO wilt pevM.itme to believe.*b*t you never knew any more of roe, than I have the Honour ot knowing of you t And if in your Letter of the itlh of Auguft you had not made wrong-Ufe of my Name, I mould not now find myfelf I j -y .obliged to enter into a Corref-A»n»i» fr«iiimn.iifopondence with you.You pretend that " in the Summer o4 the-Year 1764, i> Overtures were made in my Name to feVeral Mem* e bers of Parliament, importing that I was ready to " impeach Three Perlbns, Two of whom were Peers, " and Members of the Privy-Council, «f having fold '« the Peace to the French t" And you Item to found hereupon the Evidence of a Charge, which you fay jni carried yourfelf to Lord Halifax. 1 declare therefore, here, Sir, tlyrt I never made, tnrcaufed to be made, any fiich Overture, either in I toe Winter or Summer of the Year 1*64, nor at any ctberTnne : I was, on the one Side, too faithful to the OiliceT filled, and on the other, too zealous a Friend to Truth.I confefs you do not fay it was I that made the Overlures; but only that they were made in my Name, particularly to Sir George Yonge and Mr. Fitzherbert.1 allure you I do not know either of thefe Gentlemen, and never authorrfed any Perfon whatever to isske, in my Name, fuch Overtures, which the Abhorrence alone I have for Calumny, would make me --t.. i ..P»motic you to b« at prudent » Photic, ad therein give £** of your own sSsts^SSSrSSwtf'S our mo
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.680 | 0.569 |
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".