Bibliographic record
Abstract
The new linguistic science lacunology has come into prominence recently. The lacunae studies embrace sociolinguistics, nonverbal semiotics and applied linguistics studies. The research of terms of lacunology (lacunicon) is devoted to empty places in the structure of the contrasting languages and the ways of zero verbalization in the written and oral discourse. The empty category of the category “lacunarity” has been studied by the Canadian translators Jean Paul Vinay (1910-1999) and Jean Darbelnet (1904-1990), the Russian pioneers of lacunology Yuriy Sorokin (1936-2009) and Gulchera Bikova and their followers. The \nresearch has resulted in the cognitive mind mapping. It turned out that lacunarity has several vectors, e.g. lacunae of language (i), lacunae of speech (ii), lacunae of cognition (iii) to be further discussed in the article. The basic terms of lacunicon are described. These are a pause in syntagmatics, a linguistic gap in paradigmatics, a lacuna in both diachronic and synchronic \nregimes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".