Bibliographic record
Abstract
Increasing awareness of positionality, systemic erasure of Indigenous culture, and intergenerational trauma incites an emergence of Indigenous presence in academia. Institutions clear a path by walking with first-generation Indigenous students, bringing UN SDG 16 to fruition – the promotion of peace and sustainable development, with justice and accountability at all institutional levels. In this context, an outreach program aimed at sharing engineering concepts with Cree youth was developed. Showing the opportunity for healing through principles of control systems, energy conversion, and force balance with an interactive demonstration and authentic presentation. To focus on collaborative innovation and renewable energy, a model wind turbine was constructed. The turbine was designed to operate at a tip-speed ratio of 4.5 and power coefficient of 0.4, which defined a required 2500RPM to generate 4W of power. To generate electrical power, the turbine rotor was connected to a permanent magnet DC motor, acting in reverse as a generator for a circuit containing a 120ohm resistor and a green LED. Two options, a micro-gear motor rated 6V at 355RPM and a vibration motor rated 5V at 2550RPM were evaluated by coupling two matched motors together, one connected to a power supply and the other to the load circuit. The rated voltage was applied while recording RPM and voltage drops. Testing identified the vibration motor as the most suitable, and guided integration of the components into the turbine design. Intergenerational trauma parallels wind energy as both present an unsteady flow that can be transformed into a guiding light for subsequent generations. Understanding all system factors while honouring identity is essential for conversion. I am a woman who endures an unsteady load with the purpose of transforming it. Fundamentally, this load is fluid, transient, and impossible to predict or control, and the conversion system requires continual development to prevent losses.
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 | 0.002 | 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 | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".