Sharing Intergenerational Food Stories on the Land and Online to Engage Mi’kmaw Children in Indigenous Food Sovereignty
Bibliographic record
Abstract
Introduction: Within Indigenous cultures, stories about food and health have been shared on the land because the land, air, water, and ice are where food naturally grows and exists. Yet, Indigenous children are increasingly using online technologies to gather knowledge and share stories with their communities. Objectives: Through analyzing a storytelling session led by a Mi’kmaw Knowledge Keeper, this paper explores how land-based learning can come together with online technology to engage children in Indigenous food sovereignty. Methods: This study is situated within an intergenerational Mi’kmaw foods project called the Land2Lab Project and is guided by Two-Eyed Seeing and decolonial theory. We used narrative inquiry to explore a Knowledge Keeper’s storytelling session that was conducted with 14 Mi’kmaw children. Results: Through this study we learned that we can prioritize Mi’kmaw knowledge both on the land and online. Yet, spending time on the land intergenerationally learning about Mi’kmaw foodways is imperative to maintaining Mi’kmaw food knowledge and engaging children in Indigenous food sovereignty. Conclusion/Discussion: While online technology may seem paradoxical to land-based learning, some elements of intergenerational storytelling can happen online and on the land, and both can be used to support the protection of Mi’kmaw knowledge systems, foodways, and health for future generations.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".