Breadth and Depth Specialized Vocabulary Learning in Theology among Native and Non-native English Speakers
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".