Breadth and Depth: Specialized Vocabulary Learning in Theology among Native and Non-native English Speakers
Bibliographic record
Abstract
This article describes a case study on native and non-native English-speaker (NES and NNES) students' knowledge and learning of specialized vocabulary over one academic term in a graduate school of theology. After outlining the collection of baseline data on theological vocabulary and the development of a Test of Theological Language (TTL), the article discusses the five NNES and seven NES participants' scores on the TTL. Results on the initial TTL revealed that both groups brought some breadth and depth knowledge of specialized theological vocabulary to their studies, but that the NNES group's scores on both measures tended to be lower than those of NESs. At the end of the term the TTL results indicated an overall increase in scores, but while the gap between the NNES and NES groups in breadth vocabulary knowledge was essentially bridged, for depth knowledge it actually widened. These and other findings are discussed. Résumé Cet article fait état d'une étude de cas sur les connaissances et l'apprentissage du vocabulaire spécialisé chez les étudiants dont la langue maternelle est ou n'est pas l'anglais (NES et NNES) dans un programme de théologie au 2e cycle. Après la description du recueil des données de base sur le vocabulaire de théologie et du développement d'un test de vocabulaire en théologie (TTL), l'article traite des résultats du TTL des cinq participants NNES et des sept participants NES. Les résultats du premier TTL indiquent que les connaissances lexicales des deux groupes présentaient profondeur et ampleur, mais que le groupe NNES avait généralement des scores inférieurs. Les résultats du TTL Ã la fin du trimestre indiquent que les scores des participants des deux groupes ont augmenté ; les scores des deux groupes pour l'ampleur des connaissances lexicales sont essentiellement comparables, mais ceux pour la profondeur des connaissances lexicales révèlent une lacune parmi les participants du groupe NNES. L'article conclut sur une discussion des résultats de cette étude de cas. [End Page 175]
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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 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".