Academic presentations : exploring the second language socialization of international graduate students across disciplines
Bibliographic record
Abstract
This study examined the language socialization of international students in the graduate school context of a western Canadian university. Focusing on one pervasive speech event, academic presentations (APs), this study explored the role this socioculturally organized activity played in facilitating students' linguistic and sociocultural development, and how it aided them in negotiating their entry into the academic world. The participants in this study included 55 graduate students and nine course instructors. Thirty seven students were native speakers (NSs) of English, while the remaining 18- -the focal participants of this study—were non-native speakers of English (NNSs). The sites were seven graduate courses in six different departments in three different faculties (Faculty of Medicine, Faculty o f Arts, and Faculty o f Applied Science). A qualitative approach was employed, and thus multiple kinds of data were gathered over a four-month period. Data collection methods included: (a) open-ended interviews with participants; (b) tape-recorded observations of APs; (c) researcher's fieldnotes of APs; and (d) collection of written documents (e.g., course outlines). Data were analyzed following Bogdan and Biklen (1992) by identifying major and minor themes while iteratively going over the data. A comparison of APs across disciplines is included, examining aspects such as A P content, sequence, length, and format. As well, an analysis of the qualities promoted in each field and of the multiple purposes APs fulfil is provided and related to the complex socialization (i.e., both linguistic and sociocultural) of international graduate students. Findings of the study suggested that APs are a complex task whose meaning is not fixed, but rather is determined by the interplay of the broad context of the academic world, the micro-context of each community of practice, and ultimately by each individual. With regard to the language socialization 6f NNSs, APs challenged students in both linguistic and sociocultural ways. However, by observing, participating in, and reflecting on APs, students gained increased membership and competence within their academic communities.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".