Language in the Public Space of a Dalmatian Town: The Linguistic Landscape of Zadar
Bibliographic record
Abstract
The objective of this paper is to analyse and describe the linguistic landscape of Zadar. Zadar is one of Croatian towns that have been parts of different socio-cultural and administrative entities throughout several millennia of their history. In its history Zadar was also the final destination of many migrants and immigrants. Because of these facts, Zadar has almost always been a multilingual town and its contemporary linguistic ecology (although slowly changing) is one of the most interesting ecologies in Croatia, because of the existence of several Croatian dialects, regiolects and standard languages (Brozovic, 1976). In this paper I will present the results of the analysis of the linguistic landscape of Zadar. The analysis will be based on methodology used in previous research on linguistic landscape (Backhaus, 2007 ; Cenoz and Gorter, 2006 ; Gorter, 2006 ; Jaworski and Thurlow, 2010 ; Franco Rodriguez, 2009 ; Shohamy et al., 2010 ; Shohamy and Gorter, 2009). However, most of previous research on linguistic landscape was conducted in towns and cities comprising rival ethnolinguistic communities actively participating in the symbolic construction of public space with their choices of language on signs (cf. linguistic landscapes of Jerusalem, Montreal, Brussels, Tokyo, Rome, San Sebastian, Bangkok, and other). In Zadar, on the other hand, several ethnolinguistic communities exist (with varying numbers of members), but the results will show that the linguistic landscape does not display elements of rivalry between them. The methodology used in this research is similar to methodologies used in previous studies of linguistic landscape. The elements of linguistic landscape (Backhaus, 2007) will be photographed with a digital camera on five locations in the town (quarters Arbanasi, Poluotok, Vostarnica, Relja, and Puntamika). These locations are chosen because they contain numerous religious, administrative, municipal, educational, and juridic institutions, which is the reason why all citizens of Zadar must at some point pass through these quarters and experience the linguistic landscape. The photographs will then be analysed according to standard procedure developed by previous researchers (explained in detail in Backhaus, 2007). However, most previous studies of linguistic landscape have only taken into account the texts on the signs and the frequency of languages, but have forgotten authors and the ideological processes behind the authors' decisions. In this paper, the results will also incorporate the qualitative data collected in interviews with people actively involved in the production of signs in public space and people who are active consumers of these signs, i.e. the passers-by. These qualitative data will hopefully provide an insight into different ideological aspects of the production of language in the public space.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".