Symbolic Ethnicity: A Non-Textual Translation Process
Bibliographic record
Abstract
The translation process is commonly defined as a practice where meaning is transferred from one linguistic code to another. This poses difficulties as it excludes other meaning-making practices. By examining examples of symbolic ethnicity, I demonstrate that cultural phenomena can be considered a process of non-textual translation. To do this, I draw on Maria Tymoczkos notion of the cluster concept in order to explore the similarities and overlaps between translation and symbolic ethnicity. Furthermore, the images depicting ethnic symbols are examined using Multimodal Critical Discourse Analysis in order to address the meaning connoted in the images that contribute to the representation of Italian-Canadians. Lastly, using the Constructionist Approach as understood by Stuart Hall, my research addresses how these symbols are recognizable as Italian-Canadian. My analysis of demonstrates that symbolic ethnicity is not only a form of representation but also a non-textual translation process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.017 |
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; both teacher heads agree on what is shown here.
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".