Building Indigenous knowledge: Exploring the Pedagogy of Māori knowledge in the Digital Computing Information Technology Tertiary Sector of New Zealand
Bibliographic record
Abstract
In 2021, Computing Information Technology Research and Education New Zealand (CITRENZ, 2021) held a conference for academics to explore information technology in a changing world. It provided a platform for those academics that teach in this industry a forum to discuss knowledge transfer and teaching practices. A workshop on “Mātauranga Māori in Information Technology,” which is a specialised type of expertise that continues to be in its infancy was presented. Mātauranga Māori in academia is a body of Indigenous Māori knowledge passed down from generation to generation, stretching back to te ao marama, the creation of the world (Sadler, 2007). Therefore, the depth of Mātauranga Māori is embedded in the earth and waters that cover the lands (Royal, 1998). Exploring ways to transfer this type of knowledge to a classroom or global online environment for Information Technology is a new type of pedagogy. Building the academic capacity of people and academic programmes in Information Technology that supports Mātauranga Māori is pioneering for Indigenous academics. Navigating this pathway in the tertiary sector is delegated many times to the Indigenous academic to take leadership in this discipline. It also becomes a challenge for the Indigenous academic to retain leadership in these areas when these topics become globally attractive, like Cyber Security, where the representation of Indigenous experts are scarce in this industry and the outcome is that knowledge transfer tends to be the responsibility of the non-Indigenous academics to lead capacity building initiatives. This article discusses five key issues: 1) programmes in the Digital Computing Information Technology sector; 2) Mātauranga Māori in Information Technology; 3) the pedagogy of teaching and delivery; 4) Indigenous leadership in this sector; and 5) capacity building initiatives. It draws heavily from the literature and experience of those academics who work in the Institute of Technology and Polytechnics in Aotearoa New Zealand.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".