Using Critical Discourse Analysis to Address the Gaps, Exclusions and Oversights in Active Citizenship Education
Bibliographic record
Abstract
This paper is concerned with the analysis of the use of Latin terms in the field of human anatomy through the contrastive analysis of examples from anatomy atlases and textbooks, and research papers in the area of human anatomy in English and Serbian. The contrastive analysis of examples has highlighted a certain tendency towards the use of original Latin terms in anatomy literature in the Serbian language, while the tendency of anatomy literature in English is towards the use of English terms which most often have a Latin root. It has also been noted that Serbian literature, in addition to original Latin terms, uses a significant number of terms with a Latin root. The noted tendencies differ depending on the type of literature (anatomy atlas, textbook or research paper). A significantly greater uniformity in the use of terminology has been noted in editions in English as compared to the Serbian anatomy literature where a lack of such a uniform system is evident. Bearing in mind the ever increasing significance of the English language in the world of science, one of the conclusions of this paper is that these differences may be of practical significance for authors from Serbia looking to publish their work in English as well as for translators of medical literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".