Bibliographic record
Abstract
Beene-d Lewi• • Th s Em t!" nct! of Alo/an Tu ,.h g (Mod HO Türkiye'nin auhu r u). O d or d Uni versit y p ress, LondonNew Yc rk-Toronto, 113'8 1, •. XVh51l,a ..de d hari ta. Lo nd ra Üniversite si Pro(e.Jö,lerind en Prof. Bem a rd Lewla' Ie 7·S yıld beri üu ııd! çah; tı ll ese ri ay ın a leuı ık mı bu lcnuycr, Yeni Türkiye' nin meydana ır 1i ni ve b u :Ile Ji mede ro l o yna yan imi ıle inceliyen b u uer . bü'yük bir boş( ~u dur ak ta Eser , e'uu iti barile b ir ır j~ ve o nu taki b ede n i ki ana böiü,..,de-n meydana gelmele te rlir . Btm" Inr. bi r sonuç ti kib etmek tedirGi ri , k&!Iml {s. -ı.) Bu g ünkü - T ürlcmed eulyetin le Iı::ayn ak ları a~l ı~ tafım a k ta r. Burada, bu g ünk ü Türkiy e"· nin o JuJu oda t ernal rol oyna yan "t 1cç isla miyet '0' (1 ot ur ıl an yer" g ibi kültGr un :ı:u ıır tahlil edi lmektedi r, "O rt aya ık ın tl'Jlıalau ıı )' II D bir inci böl üm ki:!. le .ım he tiede in celeonmioti r . nlı llI A'u ıt .suleutu (s. ·~g) baıl kmı at y. sı da O!J ma nlı tuihi ak k da ,im i)' nıda r yap ıı';'., ıı ,mal arın öxet i veritmek.. t edb-, P r of. B. Lewis b u kon u üze.rind e ayık i l çalıtı lma ıtın da ti ki.yl!t etmek.. t edir-, Bu . Ia nd ..ki ça ltfmala rll1 8'z ve kif ay etsiz oIu:;;u lı mp~ to lu nun sukut aebe ble j-ini y d nl m. ı, o larak ır a!ıc mlt Y"ur iemi le rt tmpa.. ukUlı d oku y e alı aki tarihi n yrind en ba hsettikte n sen-e , OAm lı yaza arının impa ra tor luk un XV i ve XVii • ..ıı arda ar -ı iU ~ri ~h de
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".