How community context fosters sustainability in science education
Bibliographic record
Abstract
Within Canada, there is increasing concern that STEM students’ techno-centrism interferes with the societal need to train thoughtful problem-solvers. Take engineering as a case study: humanitarian engineering projects designed to help ‘developing’ communities experience high premature project failure rates. This raises concerns about the overall sustainability and longevity of engineering solutions. The theory of Contextual Engineering was established as a potential response to this high failure rate, and emphasizes community context as critical to the successful design, implementation, and functioning of an engineering project. It is unclear if the current standards and methods of teaching and learning in engineering/STEM education allow for the integration and application of concepts like Contextual Engineering into students’ future decision-making in their careers. Calls for reform to introduce more experiential and transdisciplinary methods in teaching and learning suggest that there is value in evaluating the current academic environment of STEM, to conceptualize any presence of contextual/community elements, and to determine the steps necessary to further introduce it into STEM education in Canada. Thus, our project seeks to understand the current perspectives of students about the value of contextual approaches to curriculum and program design. In this presentation, we will present some findings from a survey representing the engineering student body of a Canadian institution. This project seeks to contribute valuable insights about the current higher education climate to better inform, introduce and adapt contextualized education approaches into the engineering curriculum and STEM at-large. This project has been approved by the Guelph and Queen’s Research Ethics Boards. Be sure to bring a device (smartphone, laptop, tablet) so you can participate and engage with the presentation in real-time!
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.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".