Bibliographic record
Abstract
The distinction between VY as a verb and as a dscourse marker was first proposed by E. Konig in 1897' and is now generally accepted.W. hchter called the latter a text dei~ticon.~Unlike regular verbs, the discourse markers w.r and ;rqm need not agree in person, gender, or number with the subject of the clause to whch they are attachedS4 Nevertheless, the exact discourse function of -m/?-rn is still open to debate.The explanations cover a wide-ranging spectrum, includmg inter aka, a semantically empty temporal marker: an emphasis of the temporal setting: a marker of progress,' a connection that introduces an independent narrative or a new section: the beghung of a new narrative or a turn of the plot in the narrative: an interruption without a significant break,'' and "continuity at an intra-scene level."llOthers simply 'This is an expanded and revised version of a paper presented at the NAPH session of the annual AAR/SBL meeting in Toronto, Canada, November 26,2002.'Cited in Gesenius' Hebrew Grammar: A s Ediled and Enlarged the Late E. Kautzsch (GKC), 2d Eng.rev.ed. of the 28th German ed., trans.A. E. Cowley (Oxford: Clarendon, 1 !Jog), 327 n. 1. ' W. Richter, Gmndkzgen einer althebrijischen Grammatik.Band 3: Die Beschreibungsebenen. Der Sat? (Satqheorie) (St.Ottilien:
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".