El cuestionable empleo del gerundio en la redacción científica de los profesionales de la salud
Bibliographic record
Abstract
Background: in the scientific writing there are numerous inaccuracies that cause errors and amphibologies in the messages, including the incorrect use of the gerund, a questioned sign and even rejected by some researchers.Objective: to characterize the use of the gerund in the scientific writing of some health professionals.Methods: a descriptive investigation was carried out in the Faculty of Technology - Nursing, in Villa Clara, from20 18 to the first quarter of 2019. Theoretical methods were used: analysis-synthesis and induction-deduction; Empirical ones: documentary review as a source of primary data, questionnaire survey applied to professionals and discourse analysis in the scientific articles analyzed; and mathematics for numerical data.Results: the correct use of simultaneous and peripheral gerund was found, essentially with being; also the explanatory and ilocutive; in other situations they use it incorrectly: repeated, with subsequent action and adjective function. Some rejection was perceived towards its use in scientific documents, due to certain entrenched dogmatic conceptions, which led to questioning among researchers.Conclusions: the need for language improvement is reaffirmed, in which the use of the gerund is encouraged as an element that brings diverse nuances to the scientific text.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".