How can engineering education contribute to a sustainable future?
Bibliographic record
Abstract
In the present paper we question how engineering education (and engineering) can support greater participation and inclusiveness in decision making and science and technology. We consider the work relating to engineering and society that is conducted by the scholars of science and technology studies, but which is rarely read or considered by the engineering educators who could draw on it. We consider the results of an initial analysis of data collected from interviews with the science, technology and society (STS) faculty across North America and Europe. STS looks at how technology affects society and how society affects technology, while engineers ‘create’ technology. Consequently the current authors suggest that it would be of great relevance to both engineering and STS to join up the thinking across these two arenas by focusing on a question that is not privy to the disciplines: is how can we create a socially just and sustainable future for all? We shall consider questions asked in STS of technology (engineering), e.g. what is its impact on society, who owns the technology, what are the political artefacts of the technology and we consider their influence on engineering (education). Using a phenomenographic approach to the research, 12 categories of description have emerged. Three of these categories are highlighted in this paper: participation; politics and policy; and citizenship, as they reflected themes that are rarely ‘discussed’ in engineering curricula but which appear to be uppermost in the STS arena. Participation was described in a range of ways, from approaches to participation to the case for and against it. In politics and policy much was made of the interplay between scientists and politicians and the power and knowledge games between these two arenas. Citizenship is a hotly discussed topic and is evident in a number of government agendas. Approaches to enhancing citizenship are discussed in a myriad of ventures, e.g. through public participation, being critical of information and through education. ‘It is only the oppressed who by freeing themselves can free their oppressors. The latter, as an oppressive class, can free neither others nor themselves. It is therefore essential that the oppressed wage the struggle to resolve the contradiction in which they are caught; and the contradiction will be resolved by the appearance of the new man: neither oppressor not oppressed, but man in the process of liberation. If the goal of the oppressed is to become fully human, they will not achieve their goal by merely reversing the terms of the contradiction, by simply changing poles…’. Paulo Freire, 1970
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".