The Borders of Engineers Without Borders: A Self-Assessment of Ingenieros Sin Fronteras Colombia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it