English Lecturers’ Digital Literacy and Their Scientific Publication: Seeking the Correlation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The research aimed at seeking the correlation between English lecturers’ digital literacy and their productivity in publishing their research articles. It applied a quantitative research by correlating the variables between the online questionnaire result of English lecturers’ digital literacy and lecturers’ scientific publication data from their Google Scholar accounts and Science and Technology Index Portal or SINTA Portal of the Republic of Indonesia. The research population was all permanent English lecturers at State Islamic Higher Education in West Sumatera. There were 65 respondents in three institutions, but only 85% of participants gave feedback on the online questionnaire. The questionnaire was about the digital literacy of English lecturers in using and finding digital information and technology. The research also accounted online journal publication of each English lecturer in his/her account. To analyze the data, the research used the Pearson correlation formula. The finding reveals a positive correlation between English lecturers’ digital literacy and their research publication, as shown by the Pearson correlational coefficient, 0,48. The score lies between 0,40-0,59, which is under sufficient category. The result implies that English lecturers’ digital literacy has something to do with publication. The more digitally literate they are, the more productive they will be, even though there are other factors that influence someone to carry out the publication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it