English Lecturers’ Digital Literacy and Their Scientific Publication: Seeking the Correlation
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".