Integration of the Gender Vision in Training by Competences in Engineering
Bibliographic record
Abstract
In the present work, a proposal is presented to integrate the gender vision in the formation of social, political and attitudinal competences in engineering careers.The international call to address issues related to equal rights and opportunities for women is growing.One of these problems is the minority participation of women in engineering careers, which is due to multiple factors and is evidenced in access, permanence and graduation.This negatively impacts society with the loss of women's talents and abilities to build a sustainable world, an issue that is closely related to the sustainable development agenda of the United Nations Organization, through two of its objectives: the SDG 5, to achieve Gender Equality, and SDG 4, on Quality Education, to which the Faculties of Engineering adhere.Meanwhile, from CONFEDI in the year 2006 the generic competences are proposed, among which are the so-called Social, Political and Attitudinal, for engineering training, which were assumed by ASIBEI in 2014.The joint recognition that they carry out is also highlighted.ACOFI, LACCEI and CONFEDI to the existence of the gender gap in the field of Engineering, through the creation of the Matilda Latin American Open Chair and Women in Engineering, in 2020, and Commissions that address the issue in their own contexts.Quality education in Engineering finds an opportunity to consolidate strengths and address weaknesses, especially in instances of change of study plans and in the proximity of accreditation processes for the Argentine context, the gender gap being a challenge to consider.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".