MétaCan
Menu
← Back to cohort
Record W6893100603 · doi:10.5281/zenodo.14672691

Right way, wrong way, better way: A global model for developing the ethical engineer working with Indigenous communities

2018· article· en· W6893100603 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsIndigenousEntertainmentEngineering educationTraditional knowledgeQuality (philosophy)Displacement (psychology)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0250.059
Scholarly communication0.0180.019
Open science0.0040.035
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0110.003

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.

Opus teacher head0.061
GPT teacher head0.296
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicIndigenous Health, Education, and Rights→French-language works237,207→