Work in progress: The impact of using LATEX for academic writing: A Peruvian engineering students' perspective
Bibliographic record
Abstract
This study shows the benefits of using the typesetting software LATEX in research courses of two engineering programs from the Sciences and Humanities University (UCH) in Lima, Peru. In several universities worldwide, this software is used to write diverse types of academic documents (e.g., theses, scientific papers and books). Some of the many advantages of LATEX is the easiness of writing mathematical equations and also dealing with different citation styles such as APA, IEEE and Vancouver. Moreover, the documents produced with this software possess an impeccable professional layout. Thus, during the second semester of the year 2019 and the first semester of the year 2020, a group of systems and electronic engineering students from research courses at the UCH were introduced to LATEX and were surveyed to find out their perspectives with regards to the use of this software. By means of a self-administered questionnaire, a general consensus could be seen among the students that LATEX is far better to write academic documents than other typesetting software. Hence, starting the second semester of 2020 we have encouraged its use in other research courses and suggested as well to the head of the engineering department at the UCH to make use of this tool a compulsory one in all such courses.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".