Bibliographic record
Abstract
Promoting empowerment and growth for First Nations mothers is critical when attempting to improve the post-secondary educational attainment of Indigenous Peoples. Based on the literature, Indigenous Peoples of Canada have lower rates of University-level education across all Indigenous groups (First Nation, Métis, and Inuit). The literature also shows that Indigenous Peoples cite personal and family responsibilities as a barrier to their educational attainment more often than any other barrier. Approximately one in ten First Nations and Inuit teenage girls between the ages of 15 and 19 years were parents in 2011. Fertility rates in the same group are six times higher than that of other Canadian teens. The statistics go on to explain that early motherhood increases the vulnerability of young First Nations women who are already disadvantaged socio-economically by their cultural background and gender. The data for this project was collected through the use of autoethnography and Indigenous storytelling as methods. Together, we explore the literature and the shared stories, while discussing the preliminary project findings through a decolonizing lens. Key points discussed include the balancing of identities, the implications of the imposter syndrome for First Nations Peoples, the process of navigating the post-secondary institution, and the importance of restoring culture while finding autonomy within academia. This research aims to contribute to the literature on Indigenous education while creating the groundwork for future research which may help to inspire future generations of First Nations mothers to attend post-secondary education.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".