Abbreviations, acronyms and contractions
Bibliographic record
Abstract
Abbreviations, acronyms and contractions 1 first person 2 second person 3 third person α alpha (significance level) ACC accusative case ACT active ADJ adjective ADV adverb(ial) ANIM animate AOR aorist ARG.Cro speech recorded of Croatian-speakers in Argentina ART article ATTRIB attributive AUS.Cro speech recorded of Croatian-speakers in Australia AUT.Cro speech recorded of Croatian-speakers in Austria AUX auxiliary BGLD.Cro speech recorded of Croatian-speakers in Burgenland (Austria) CAN.Cro speech recorded of Croatian-speakers in Canada CL clitic COLL collective or mass noun COMP complementizer COND conditional CONJ conjunction COP copula Cro Croatian DAT dative case DEF definite DEM demonstrative DET determiner DIMIN diminutive DIR direct ED.PTC editing particle Eng.English F feminine gender FEM.1 feminine nouns of the first class (mostly ending in -a) FEM.2 feminine nouns of the second class (ending in a consonant) FNRJ Federativna Narodna Republika Jugoslavija 'Federal People's Republic of Yugoslavia' (1945-1963) FUT future tense GEN genitive case Gen.1 first-generation Croatian speakers (i.e.those born in the homeland) Gen.1A first-generation Croatian speakers who emigrated in late adolescence or as adults
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.169 | 0.113 |
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".