Bibliographic record
Abstract
ABSTRACT: An analysis of authentic or genuine interactions among Cameroon English speakers reveals that conversational routines in this variety of English differ a good deal from those obtained in other varieties of English, non‐native varieties of English inclusive, and more specifically in native varieties of English. This paper looks at ‘thanking responders’ in Cameroon English speech. The examination of real conversational exchanges between many Cameroon English (CamE) speakers and myself between October 2002 and July 2003 demonstrates that speakers of English in Cameroon use a wide array of expressions to acknowledge thanking, ranging from no acknowledgements at all, namely absolute silence, to such expressions as thank you, thank you too, for nothing, mm, yes, all right, ok and so on which are not very common or attested in native varieties of English. In other words, these expressions which are widespread in Cameroon are not heard too often in other varieties of English while speakers acknowledge thanking. Pragmatically, it is suggested that these thanking responders in CamE somewhat reflect the cultural values of Cameroonians who, in addition to English, speak other African local languages in which thanking and thanking responders are not necessarily similar to those in English. From a pedagogical viewpoint, the paper proposes that the native English forms of this aspect of daily interaction be taught to CamE speakers in the event of cross‐cultural communication and also strongly advocates that local or Cameroonian forms not be considered substandard but introduced in the school curriculum and accepted as appropriate for the local or regional variety.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".