The Sociology of Training in the Digital Age: New Engineering and Professionalization
Bibliographic record
Abstract
This study aims to analyze how work digitalization and technological innovations influence the training engineering and professionalization processes in the Bachelor's degree in Sociology. A qualitative methodology based on Sociotechnical Discourse Analysis (SDA) was used to examine the relationship between technology and society and how these dynamics affect academic training in Sociology. The main findings reveal significant institutional resistance to adopting digital technologies (DT) and curricular changes, as well as a perception of digitalization as a temporary solution. Barriers such as gerontocracy, inadequate technological infrastructure, and an incentive system that discourages collaboration were identified. The conclusions underscore the urgent need to modernize the Sociology curriculum to include advanced digital competencies and foster a culture of innovation. It is recommended to implement pedagogical strategies that include the DT transversal use, continuous training programs for teachers, and a comprehensive curricular reform.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".