Supporting Communities in Caring for Salmon and Each Other: Creek Restoration as a Site for Multi-System Change and Wholistic Re/conciliation
Bibliographic record
Abstract
This paper describes a unique collaborative action research project that brings together members of the q̓íc̓əy̓ (Katzie) First Nation, post-secondary and K-12 communities, as well as foresters and environmentalists, to restore creeks that have been compromised by land use impacts, forest removal, and global warming. Identifying creek restoration as a site for multi-system change and wholistic re/conciliation, we explored the following questions: How can we bring together members of our diverse communities to learn about the dire condition of our watershed and take action to help Salmon? How might this collaborative work strengthen community relationships? What contextual factors enable and impede the enactment of our vison? Through iterative cycles of action and reflection, intentional trial and error, conversational inquiry, and storytelling, we identified ‘guideposts’ that will inform our work moving forward. Our research has illuminated structural changes that could enhance environmental justice for Salmon, such as empowering the caretakers of creeks and rivers since time immemorial as sovereign leaders of restorative projects, affirming the rights of the Land and other sentient beings to receive care, developing leadership structures that serve to unite (rather than polarize) citizens in addressing environmental problems, and forming diverse relational webs that exceed partnerships. Action research, informed by Indigenous worldviews, can play a pivotal role in supporting communities in assuming relational responsibility in caring for the Land and one another. As Donna Haraway (2016) contends, it is time to ‘make kin’ outside of our genetic and ancestral ties to ‘change the story’.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".