Bibliographic record
Abstract
This article presents independent morpho-syntactic evidence from Ancient Greek and Old English supporting the existence of two alternative Aspect functional heads (following Fukuda 2007 on Modern Japanese and Modern English). The focus of the study is on the similarities between Ancient Greek and Old (and Modern) English aspectual verbs and on the consequences of these similarities for the analysis of aspectuals. Ancient Greek and Old English aspectual verbs fall into two groups: (a) aspectual verbs that could select both infinitive/to-infinitive and participial/ bare infinitive complements (aspectual in H-Asp), and (b) aspectual verbs that selected only a participial/bare infinitive complement (aspectual in L-Asp). No aspectual verb takes only infinitive/to-infinitive. Furthermore, “long middles/passives” is an option only with aspectual verbs in L-Asp, while the regular embedded middle/passive is the only option with an aspectual verb in H-Asp. The similar properties of the Greek and English aspectual verbs, however, historically manifest diferent developments: English not only retained Old English possibilities (to- vs. bare infinitives), but later extended them from Middle English into the 18th century, while in Greek the development of the infinitive and the participle afected the options of verbal complements of aspectual verbs.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".