First Nations partnership modules for cross-cultural skills in Engineering and IT.
Bibliographic record
Abstract
Deakin University’s faculty of Science, Engineering, and Built Environment (SEBE) has a proactive agenda to increase First Nations staff and student participation. This agenda is driven by the Deakin Indigenous Strategy that includes the domains of students, knowledge, employment, whole of university and Community (Deakin University, 2023) which is enacted locally through our SEBE Indigenous Compact. The measure of success of our Compact is an increase in First Nations staff and student participation. As part of this initiative, I partnered with First Nations colleagues to develop three modules called Being in Partnership with First Nations people that are scaffolded across Engineering and IT undergraduate courses in SEBE. The modules were developed with L-J Singh (Quandamooka & Wiradjuri) and Janine McBurnie and included videos of yarning about being in partnership. These videos talk about First Nations ways of being and doing while bringing students into those ways of being. They begin by giving context by talking about history and colonial harm and include activities to reflect on being in partnership, creating future goals, and have opportunities for students to share thoughts and resources with each other. The modules are a meaningful part of the discipline content and are assessed. For example, one IT module includes work on considering using First Nations images in websites, understanding ICIP and copyright. A first year Engineering example includes building reciprocal relationships and using Heritage Acts with a requirement in student project to understand and recognise Country and Community. We focus on Engineering and IT students’ cross-cultural skills in the curriculum as knowing how to work with First Nations people and on Country is a core disciplinary skill and makes it an everyday part of student’s practice. Working with unit chairs on the modules for their units includes a process of growth and learning. I am learning a lot by being part of this project, including ensuring First Nations voices are heard, that curriculum needs to be relational as possible, to listen deeply and with my spirit. These modules are part of an initiative to increase participation by changing the way staff and students do things in Engineering and IT, and for First Nations staff and students to be able to see themselves in the curriculum. The modules are more than a change the curriculum, they foster relationships between people, making safe environments for First Nations staff and students.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.286 | 0.089 |
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".