Dr. Mosaik: a holistic framework for understanding the English tense–aspect system based on ontology engineering
Bibliographic record
Abstract
Gaining mastery of the English tense–aspect system, or the appropriate use of the different verb forms, is a significant challenge for learners of ESL/EFL (English as a Second/Foreign Language) (Larsen-Freeman, Kuehn, & Haccius, 2002 ). Knowledge of English, an international language for communication, has also become increasingly necessary for a number of adult university learners. This is true of many learners who choose to study abroad or immigrate to countries where proficiency in English is necessary to pursue higher studies and enter the workforce. With respect to verb system understanding and mastery, it is not unusual for university learners enrolling in ESL classes to have to assimilate a substantial amount of information within a limited time frame. The reality of such classes–that bring together students of different native languages and English-learning backgrounds–is that they are subject to time and curriculum constraints; it is not always possible for them to be brought to level on this particular topic. Technology-assisted language-learning applications, for their part, need to provide many explanations on verb tense uses; at more advanced levels of practice, these can be numerous (e.g., Englishpage, 2021 ).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".