Bibliographic record
Abstract
xkaèkhoknh jk"Vªh; vkanksyu] tks Hkkjr ds bfrgkl dk ,d egRoiw.kZ dky[kaM Fkk] Lora=rk ds fy, ,d jktuhfrd la?k"kZ ls dgha vfèkd FkkA ;g ,d cgqvk;keh lkekftd Økafr Fkh ftlus Hkkjrh; lekt ds rkus&ckus dks xgjkbZ ls u;k :i fn;kA gkyk¡fd bldk çkFkfed mís'; Hkkjr dks fczfV'k vkSifuosf'kd 'kklu ls eqä djkuk Fkk] ysfdu vfgald lfou; voKk vkSj tu&vkanksyu ij vkèkkfjr bldh dk;Zç.kkyh us vutkus esa gh fofHkUu lkekftd {ks=ksa esa egRoiw.kZ ifjorZuksa dks mRçsfjr fd;k] fo'ks"k :i ls efgykvksa ds thou dks çHkkfor fd;kA bl vkanksyu us "vutkus esa gh efgykvksa ds thou dks ?kjsyw nk;js ls ijs] {ks=h; lekt] f'k{kk vkSj jktuhfrd {ks= esa :ikarfjr dj fn;k" bldh fojklr ds ,d egRoiw.kZ] fQj Hkh vDlj de vk¡ds x, igyw dks mtkxj djrk gSA ;g ifjp; bl 'kksèk fo"k; dh çklafxdrk dk vUos"k.k djds djsxk fd dSls xkaèkh ds n'kZu vkSj muds }kjk lapkfyr vkanksyu us efgykvksa ds fy, vHkwriwoZ volj vkSj pqukSfr;k¡ iSnk dha] mUgsa lkoZtfud thou esa vkxs c<+k;k vkSj lkekftd ifjorZu dh ,d nh?kZdkfyd çfØ;k 'kq: dhA bl çHkko dk vè;;u Hkkjrh; jk"Vªokn dh tfVyrkvksa] ySafxd Hkwfedkvksa ds fodkl vkSj ledkyhu Hkkjrh; lekt ij xkaèkhoknh fopkjksa dh LFkk;h fojklr dks le>us ds fy, vko';d gSA
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.329 | 0.286 |
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".