Perceived Gender wise Judgement of NETs on English Language Errors
Bibliographic record
Abstract
The purpose of this study entitled 'Perceived Gender wise Judgement of English Language Errors' was to evaluate and determine the gravity of English language errors in terms of acceptability and intelligibility by the native English speaking teachers. Quantitative research methodology was utilized in this study. An error evaluation questionnaire consisting of 100 questions which were collected from the works of the higher secondary school level students was prepared with a Likert scale survey. The questionnaire was mailed to the teachers of colleges and universities of the native English speaking countries and 100 useable surveys were received electronically for a response rate of 50%, which is a good response rate for a mail survey. Received responses were analyzed using an SPSS programme and explained descriptively. The result of the study revealed that country wise native English speaking teachers judge the ESL errors alike in acceptability judgement whereas in intelligibility judgement, female teachers are found to judge the errors slightly ahead by 3.49%. Overall, both these sub-groups of teachers have perceived the errors almost similar. Moreover, the results reveal that out of the five country English speaking teachers, Australian male teachers have shown their most severity in evaluation of errors and the New Zealander teachers employed lenient patterns in their evaluation patterns. Likewise, the most severe male teachers are the Australian teachers (77.55%/58.78%) and the most severe female teachers are the New Zealander (70.96%) and American (70.12%/56.13%) teachers whereas the most lenient male teachers are the Canadian teachers (56.62%/44.10%) and the most lenient female teachers are the Australian (65.98%) and Canadian (50.84%) teachers. It is recommended that the native English speaking teachers’ evaluation of the learner errors should direct to formulate a common error evaluation pattern which can be utilized in the classrooms so that the teachers of English become aware of such universal rating scales of the ESL errors.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".