The Factors Affecting the Sociolinguistic and Strategic Competencies in English among Teachers in Higher Education Teachers in Lipa City
Bibliographic record
Abstract
The study attempted to describe the factors affecting the sociolinguistic andstrategic competencies in English among teachers in Higher Education Institutionsin Lipa City and determine the relationship between the demographic characteristicsand their assessments. The study was done during the school year 2010-2011. ThreeHigher Education Institutions in Lipa City were selected as the research locale. Totalenumeration was used as sampling for the study. Both quantitative and qualitativeresearch methods were employed. The respondents agreed that the factors of contextof acquisition, accommodating speech norms and code switching, degree of contactwith second language users and level of confidence affect their sociolinguisticcompetence. Likewise, they also agreed that the factors of questioning skills and useof non-verbal communication affect their strategic competencies. Variations in termsof the relationships of different demographic characteristics and their sociolinguisticand strategic competencies were also established. A general sense, the teachers' diverse characteristics generated different points of view on how the factors affecttheir competencies. This led to the conclusion that they are the ones responsible why the factors influence their competencies. They should be the ones responsible for affecting culture and not culture to affect their language competencies. It is of greatimportance that teachers should take the initiative to study and systematically use thecompetencies which they can work on. HEI administrators should offer professionaldevelopment seminars as these are necessary for the effective use of the teachers' competencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".