Locally Everywhere: Production Cultures of Localization
Bibliographic record
Abstract
Into the second decade of the 21st century, global media distribution is increasingly defined by platforms. This dissertation examines media localization by looking at three distinct production contexts, and analyses the social role that localizers play in promoting global media content. The localization of global media involves interactions among three key social actors: media companies, exemplified by platforms; governments, represented by policymakers; and finally, the focus of this dissertation, localizers, who function as conduits for channeling content to the general public. Employing a blend of ethnography, interviews, and cultural policy analysis, this study seeks to provide a thicker description of how localizers create a sense of locality, and how they perceive their own roles within broader systems of media circulation. Each of the three case studies presented in this dissertation represents a distinct subculture within media production. The Quebec dubbing industry and its politics of nation and dialect provides a starting point, as a traditional example of localization. Here, dubbers not only provide translation services but also position themselves as uniquely attuned to the sensitivities of local populations. I then follow with an examination of Indigenous mainstream media producers, and their attempts to broaden their reach to global viewers by “internationalizing” their content. This process is similar to the idea of localization because it also requires careful adaptation of content for its desired audience. The third case study is of fan translators and anime commentators, whose work facilitates the adoption of global anime into local contexts by providing customized promotion. The towering presences of platforms and cultural policies are never too far from the discussion, modulating in powerful ways the work and professional aims of each of these groups. Through these snapshots, the aim of the dissertation is to highlight the role of localization and its practitioners in creating a sense of the “local” in mediascapes defined by free and unrestricted flows of information.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.038 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".