Right way, wrong way, better way: A global model for developing the ethical engineer working with Indigenous communities
Notice bibliographique
Résumé
For eight years, a team of Aboriginal and non-Aboriginal staff at the University of South Australia (UniSA) have embedded Aboriginal content across a STEM-based Division. In 2016, a group of Aboriginal and non-Aboriginal women developed, and then piloted, a 'digital extension' of this approach with the 'Blue Wren' STEM, Cultural Understanding and Aboriginal Communities [1] portal. The centrepiece of this portal is the Blue Wren Sports Association problem-based learning collection of vignettes. The profession of engineering intersects with Aboriginal Australians in many ways. Engineering in remote areas often imposes 'whitefella' (non-Aboriginal) solutions which can mean the difference between improving quality of life (where consultation is done well) or imposing irrelevant, costly and unsustainable solutions (where consultation may have been done poorly) [For example 2]. In Adelaide, South Australia, engineering works proliferate along the banks of its River Torrens (Karrawirra Pari). They include a weir; a hospital; an entertainment precinct; sewers; storm water run-off; a sports oval; a railway; floating barrages; pathways and footbridges. Until recently, most have been developed without consultation with the local Kaurna custodians, despite the river's historical, cultural and life-giving role as a food source and gathering place. This lack of due regard is unsurprising, given historical attempts by Governments to marginalise Aboriginal voices through 'White Australia' policies – displacement from land and stolen generations. A 'cult of forgetfulness practised on a national scale' has denied past wrongs and custodianship of country [3]. By the time an engineering student arrives at university, they have received relatively little by way of education about Australia's First Nations people [4]. This omission in formal learning is mirrored in other countries. For example, where 87% of textbook references to Native Americans pre-date the 1900s [5]. At UniSA a recent survey of engineering students (n26) associated with the study presented, found 73% cited most of their learning was derived from sources other than high school. They demonstrated a superficial or inaccurate understanding such as 'They [Aboriginal people] are not interested in making friends with non-Aboriginal people' to 'They have brown skin' to 'They play digeridoos'. As engineering educators, it behoves us to develop graduates who can implement solutions with full and informed community consultation and 'work with communities instead of unto' [6]. This obligation is globally linked to the United Nations Declaration on the Rights of Indigenous Peoples [7], and mirrored by the Australian professional accrediting body, Engineers Australia [8] and the Reconciliation Action Plans of Engineering firms [for example 9]. This paper presents an evaluation of the first implementation of the Blue Wren resource. The pilot took place in a core undergraduate course, Sustainable Engineering Practice and examines qualitative changes in student perceptions of working with Aboriginal Australians through looking at student writing.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,025 | 0,059 |
| Communication savante | 0,018 | 0,019 |
| Science ouverte | 0,004 | 0,035 |
| Intégrité de la recherche | 0,009 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».