It’s Tandem, not Tinder! Interrogating Authenticity and Trustworthiness of Language Exchange Applications in Adult Learners: A Central Asian and Middle Eastern Perspective
Bibliographic record
Abstract
Language exchange is based on teaching (the native language) and learning (the foreign language) in tandem. There are numerous language exchange applications (LEAs) on smartphones that connect language exchange partners from all over the world. This study investigates the trustworthiness of these applications and whether they are genuinely used for exchanging the target language or used as a camouflage for finding friends and building relationships. The study was conducted using a case-study approach focusing on two identical language exchange applications. Research tools included questionnaires and observation. The participants were active LEA users and included male and female language learners. The empirical data collected from LEAs and the qualitative data analysis will first look into application authenticity, user honesty and the most common misuse of the LEAs. It then attempts to gauge users’ attitude towards LEAs. Finally, it puts forward some recommendations for implementing LEAs amongst application developers, educators and adult learners.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".