Bibliographic record
Abstract
Armenia News ArmeniaNow Baltic Course CAD (Caucasus Analytical Digest) CACI Analyst (Central Asia and Caucasus Analyst) Civil Georgia Defense News DW (Deutsche Welle) EDM (Eurasia Daily Monitor of the Jamestown Foundation) EUObserver EURACTIV.com EurasiaNet EurasiaNet Weekly Digest Euronews Inside Europe Lithuania Tribune LragirAM (Armenian Open Society Foundation) NATO News NEE (New Eastern Europe) NewEuropeOnline openDemocracy RAD (Russian Analytical Digest) RFE/RL (Radio Free Europe/Radio Liberty) TOL (Transitions OnLine) TOL Weekly (Transitions OnLine Weekly) ValdaiClub Window on Eurasia AFP (Agence France-Presse) AIS Moldpres (Agentia Informationala de Stat “Moldpres”)—Moldova Al Jazeera AP (Associated Press) APR (Armenia Public Radio) BBC (British Broadcasting Corporation) Bloomberg Business Der Spiegel—Bulgaria FOCUS Information Agency Interfax—Ukraine Izvestiya—Russia Kommersant—Russia Lithuania Tribune Moscow Times Newsweek NYTimes—New York Times Respublika Tatarstan—Tatarstan, Russia Reuters Reuters Canada Sovetskaya Tatariya—Tatarstan, Russia Telegraph—UK Vatanym Tatarstan—Tatarstan, Russia Voice of Russia Vremya i Dengi—Tatarstan, Russia
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.766 | 0.809 |
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".