The Borders of Engineers Without Borders: A Self-Assessment of Ingenieros Sin Fronteras Colombia
Bibliographic record
Abstract
This article results from a process of self-assessment within Ingenieros Sin Fronteras Colombia (ISFC). The activities usually referred to as humanitarian engineering, assistive engineering, engineering for aid, and/or engineering for development are increasingly involving educational frameworks, activities, and institutions in service-learning schemes. In this article, we discuss the issues and challenges that arise from this combination of objectives, activities, and institutional settings, especially when these approaches are implemented in the Global South. To do so we reflect on the type of service learning we are conducting in Colombia. We develop a general service learning in engineering typology to situate our work. We find that our Local Learning in the South collaboration makes the work of ISFC both different than and similar to other service-learning engagements. It is different in the sense that local engagements do not experience the cultural and language barriers faced by cross-cultural projects. It is similar in the sense that, with the exception of the cross-cultural challenges, our projects run the same risks as any other service learning in engineering projects in the world. To reflect on these risks we propose a set of five questions to self-assess our work. Thinking about the choice of naming our work “ingeniería sin fronteras” (engineering without borders), we consider what kind of borders we are dealing with and propose five: financial, epistemic, engineering educational, knowledge, and reputation. We invite other organizations to question the kind of borders their work aims at eliminating but risks replicating.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".