The Effect of Including Literary Texts in English Language Instruction in Slovenia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article provides an overview of the Slovenian syllabuses for English as a school subject over the past 80 years, focusing on literary texts as content. The findings are correlated with trends in ELT of the relevant periods, which are typically mirrored in the contents and structure of formally approved and widely used coursebooks in Slovenia. The results are compared with recent trends in ELT in Slovenia using two methods: a) an analysis of the extent of literary content featured in a selected corpus of formally approved contemporary English coursebooks used in Slovenian primary schools, and b) an analysis of Slovene primary school students’ success in standardized testing. Finally, a synthesis of these analyses is provided to shed light on the effectiveness of including literary content in ELT, as reflected in the primary school students’ standardized test scores.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it