Negotiating Multiple Audiences of L2 Learners on Facebook: Navigating Parallel Realities
Bibliographic record
Abstract
As social network sites have become popular with university students, it is easier to understand how students employ social network sites seamlessly in their academic and personal lives. L2 learners often employ Facebook to improve their English language proficiency by communicating with their native and non-native English speakers. Facebook is considered as collapsed contexts where L2 learners navigate with their numerous multiple audiences at the same time. The study investigated the strategies L2 learners negotiate multiple audiences on Facebook. The study employed a qualitative multiple case study of three L2 learners who were Facebook users. The participants’ Facebook accounts were observed for 14 weeks, and they were interviewed using semi-structured interview. The findings of the study suggest that L2 learners use four strategies to navigate their multiple audiences such as participating in closed Facebook group discussions, only commenting on relevant posts, constructing different online identities and choosing the language of the posts and comments depending on the audience. The audience management strategies used by L2 learners are determined by the informants’ personal preferences.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".