Diversity of Research Participants Benefits ESL/EFL Learners: Examining Student-Lecturer Disagreements in Classrooms
Bibliographic record
Abstract
<p>Reviews of literature made manifest that native English speakers who were research participants in many studies on disagreements were Americans (e.g., Beebe &amp; Takahashi, 1989; Takahashi &amp; Beebe, 1993; Dogacay-Aktuna &amp; Kamisli 1996; Rees-Miller, 2000; Guodong &amp; Jing, 2005; Chen, 2006). The utmost use of Americans as research participants presented a rather restricted view on how the disagreements could be expressed by native English speakers. These studies exhibited that Americans in a classroom context normally began their student-lecturer disagreements with a positive comment (e.g., <em>‘The idea is interesting but…’</em>). Based on these results, the ESL/EFL learners might over-generalize from Americans to other groups of native English speakers and consequently postulate that all native English speakers initiate their student-lecturer disagreements with an optimistic remark. This current study chose a group of 13 Canadians and investigated their disagreement strategies in the identical context. The data were collected by videotaping the participants’ classroom for three hours every week for five consecutive weeks. Results showed that the participants normally disagreed with their lecturer explicitly but mitigated their explicit disagreements with some justification (e.g., <em>‘No because…’</em>). The findings underscored that Americans and Canadians did not normally use the same disagreement strategies in the classroom context. If future studies increasingly use British English, Australians, New Zealanders or South Africans as research participants and investigate their expressions of student-lecturer disagreement, the ESL/EFL learners will be more highly aware of differences across all native English speakers. In other words, they will be able to avoid over-generalizing from Americans to other native English speakers.</p>
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".