CLIL Funerary Archaeology courses for first-cycle and second-cycle degree students
Bibliographic record
Abstract
This paper reports on the differences between two specialized funerary archaeology courses conducted by a native language teacher from the Institute for Computational Linguistics of the National Research Council in Pisa and a subject specialist in paleopathology and funerary archaeology from the Division of Palaeopathology, Department of Translational Research on New Technologies in Medicine and Surgery of Pisa University. Lessons addressed to first cycle three-year Bachelor's degree undergraduates who were studying archaeology, art history, natural and environmental sciences took place in the second semester of the year 2012-2013. Classes in the same discipline and addressed to students from the same faculties had been held a year earlier for a second cycle twoyear Master's degree course. The classes were delivered in English using CLIL (exploitation of a \nvehicular foreign language to teach a special subject) associated with blended learning methodology \n(combination of face-to-face instructor-led training with web-based technology). Appropriate teaching materials selected by the two teachers covered a wide range of topics, from the study of death to ancient burials, rites, and dynamics of human settlements, as well as evidence of past human societies recovered by excavations. In particular, ancient Roman funerary customs (inhumation, \ncremation) and Medieval mortuary practices and burials were studied, alongside artifacts such as weapons, jewellery, and pottery vessels recovered from archaeological sites both in Italy and in Britain. Collaboration between language teacher and subject specialist was crucial for the selection of the reading and listening materials, for the correction of the oral and written work assigned to the students, and for the intervention on the part of the subject teacher to clarify points that had been raised, to assist the students during the individual presentations, pairwork or group discussions, and to encourage their work. Two researchers collaborating with the subject specialist also contributed to the lessons by presenting studies they had performed in their area of expertise and by assisting the students during the discussions. These student-centred tasks were aimed at accomplishing important educational goals such as student motivation, improved cognitive and academic performance, \nenhanced access to online learning resources, peer learning and collaboration. The 2012-2013 course \nproved to be much more interactive and challenging than the previous one, owing to the major emphasis given to the more practical aspects, in preparation for the fieldwork in archaeology and bioarchaeology, which was carried out in the summer of 2013, working with their peers from Ohio \nState University and other Universities in the USA, Canada and Australia. Particular attention was devoted to the language of funerary archaeology, and the trainees extracted definitions from the texts they were using to enrich an ongoing English-Italian glossary of funerary archaeology terms. The most important items and sentence structures of the English language were studied and revised, and an English grammar containing contextualized examples drawn from specialized works in that domain was enriched with new material. Student exchanges under different European and international \nprogrammes have emphasized on the need for specialist knowledge in specific thematic areas, alongside an oral and written command of a foreign language.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".