Analysis of the (Co-)Construction of Cultural Belongings in Several Episodes of a Korean-American Podcast
Bibliographic record
Abstract
Since the beginning of the 21st century, the “transnational turn” (Pence & Zimmermann, 2012, p. 495) in social, literary, and linguistic studies can be observed as a response to new forms of mobility and global interconnectedness, facilitated by rapid developments in the technological field and the spread of mass media and social media (Noh et al., 2013). This sociolinguistic study therefore investigates a specific small part of social reality construction (Gumperz & Cook-Gumperz, 1996) in one such digital and transnational new domain: in podcast conversations publicly available on YouTube, in particular four episodes of the show “Get Real” produced by Dive Studios, involving speakers with American, Korean, Canadian, and German affiliations. Using the method of interactional analysis (Imo & Lanwer, 2019), I investigate how the speakers co-construct their cultural belongings as individuals and as a group in the conversations through (dis)claiming cultural and linguistic knowledge. The analysis employs Positioning Theory (Davies & Harré, 1990; Harré & Van Langenhove, 1991; Van Langenhove & Harré, 1994) combined with the expert-novice model (Reichert & Liebscher, 2012), examining negotiations of levels of epistemic authority that construct speakers’ belonging to certain abstract cultural group identities, which builds on a conceptualization of culture “as an inventory of knowledge” (Busch, 2009, para. 44). Findings of the analysis reveal the embeddedness of the podcast in the specific ethnographic context of transnational Korea. The mapping of the discursive moves of the speakers in diagrams shows how the speakers relationally position themselves as belonging to more than one cultural community, co-constructing their own transnational new space that is both part of and in-between other larger cultural spaces. While doing so, they still move within the allowances and constraints of ‘transnational Korea’, drawing on its discourses and established personas and constructing different types of Other while constituting themselves as “authentically belonging” at the same time. Their concurrent embeddedness in global social phenomena such as the Korean Wave as well as being steeped with local Korea-specific discourses and ideologies is one of the findings of this thesis. Moreover, the subject position of “transnational Korean” or “Korean” appears to be closed off to certain people groups based on such local ideologies. This can be seen in the troubling effect of a German American immigrant speaker, whose co-membership in the Korean group identity is largely denied. These findings show that transnational German research, as suggested by Pence and Zimmerman (2012), will have to consider local ideologies and ethnographic constraints applying to the social space that the German abroad navigates and relates to.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".