Bibliographic record
Abstract
Fascicle I of this new dictionary (hereinafter ESJS) first appeared in 1989. Indicating that ...in the Indo-European etymological literature Old Church Slavonic material is the most frequently cited comparative Slavic material [1989:1; translation mine RAO] the authors announced that their work sought to fill the need for an etymological dictionary of Old Church Slavonic (OCS). Many available dictionaries of OCS, e.g., those by Sadnik and Aitzetmuller (1955) and Lysaght (1978), contain none. It might seem surprising that such a gap has lasted for so long, but this is indeed the case. Over the years the etymological dictionaries of Slavic languages so far produced, many of them excellent, have tended to cover a reconstructed Common Slavic, or even BaltoSlavic (Trautmann, Siawski, Trubaoev), or individual modern Slavic languages (Vasmer [Russian], Machek [Czech], Bruckner, Siawski [Polish], Mladenov [Bulgarian], Rudnyc'kyj [Ukrainian]). Many scholars have been comparatively lax about Old Church Slavonic material per se. Thus, for example, one finds statements such as ...most OCS words serve very nicely as formulas for PS1 [Proto-SlavicRAO] words (Lunt 1974: x). As a result, the concepts of OCS and Common Slavic have sometimes been confused. This is an easy trap to fall into in Slavic studies. As several authors have pointed out (most recently Lunt in a series of articles [1975, 1987, 1990]), the earliest Slavic shows very little dialectal differentiation: there are very few differences between OCS and, e.g, Old East Slavic (Old Russian). Lunt 1987: 150 calculates that ...ER [Early Rusian; Old East Slavic] shares with OCS at least 95% of the syllables in any sample exceeding a thousand syllables. In this context it is often forgotten that OCS proper is based on material gleaned from a small, limited corpus. In ESJS, the range of sources consulted is exhaustive: Fascicle 1 includes a 35-page bibliography which is later augmented. That alone imparts considerable value to the dictionary. Moreover, its entries revolve round material actually attested in these texts.1
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.059 |
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".