America in Rome: Race, Stereotypes, and Cultural Identities in the Series Home Sweet Rome!
Bibliographic record
Abstract
“Home Sweet Rome!” (2023–to present), a Canadian–Italian co-production aimed at a young audience, provides an emblematic case for examining the circulation of cultural identities and racial representations in contemporary transnational media. The series follows Lucy, an American teenager who moves to Rome with her father and his new Italian wife. The series also introduces audiences to Charlotte, Lucy’s Franco-American and Afro-descendant classmate, who embodies a transatlantic identity that challenges traditional narratives of ethnicity and belonging and functions as a crucial counterpart to the protagonist. Drawing on cultural studies and Black diaspora studies, this article analyzes key sequences selected from several episodes, with a focus on framing, editing, music, and online audience reception. Positioned within the context of international co-productions and platform-based production models, Home Sweet Rome! emerges as a hybrid text mediating between local and global logics. The aim of the article is to show how the series, while following the conventions of preteen entertainment, challenges stereotypes and explores complex dynamics of cultural identity and social integration. In this sense, Home Sweet Rome! becomes a site of negotiation for transnational identities and new, albeit ambiguous, forms of inclusion within European media culture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".