Fillers as Communication Strategies Among English Second Language Speakers in Job Interviews
Bibliographic record
Abstract
Good mastery of English in job interviews does not only give an added value to the second language (L2) interview candidates but also increases the chances to be employed. However, not all English as Second Language (ESL) speakers are competent in using the language. In this regard, communication strategies (CS) are useful for L2 speakers in overcoming the difficulties in communicating their intended messages. The objective of this study is to examine the use of fillers as CS by interviewing candidates of academic staff recruitment at Universiti Teknologi MARA Machang, Kelantan, in Malaysia. The data of this qualitative study were obtained from observations through video-recorded oral interactions between candidates and panellists during interview sessions. The NVivo software (version 12) was used to help the researcher in analysing the oral data. The results revealed that the use of fillers stipulated in Dörnyei and Scott’s (1997) taxonomy of CS was extensively used by ESL speakers in real job interviews as a processing time pressure-related strategy when the speaker was trying to fill in the gaps between their limited resources and message conveyance in L2. In conclusion, fillers are useful to L2 speakers by helping them to maintain conversations and prevent communication breakdown.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".