Academic Discourse Socialization, Scaler Politics of English, and Racialization in Study Abroad: A Critical Autoethnography
Bibliographic record
Abstract
In this age of rising animosity to newcomers in host societies, study abroad students are often reported to receive maltreatment and discrimination. To this end, I conducted a critical autoethnographic study that responds to the trajectory of my English language learning in the UK and explores my adjustment difficulties and factors such as racialized linguistic discrimination. It also reveals the types of agency that I employed in the process of academic discourse socialization and unpacks causes and processes of renegotiating and reconstructing my identity as a learner and user of the English language. The data for this study was gathered from Facebook posts, written assignment feedback, and my personal narratives and memory. The study reveals that upon finding myself in a community different from what I had imagined prior to my sojourn and with contested power dynamics between local peers and international students in classroom discourse socialization, I became disappointed and stressed and that, in turn, obstructed my learning process. However, my personal investment and agency later led me to develop my own community of practice with those who shared similar linguistic and cultural backgrounds. Meanwhile, I received what seemed to me to be racial discrimination based on my identity as a non-native speaker of English, which was the result of a scaler politics of English and perhaps blatant racism toward a student of a third-world country that saw my use of English as inferior. Therefore, the study invites institutions in host countries to reflect on their language orientation and how it is responsive (not responsive) to newcomers.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| 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".