‘A change of heart’: Indigenous perspectives from the Onjisay Aki Summit on climate change
Bibliographic record
Abstract
In June 2017, the Turtle Lodge Indigenous knowledge centre convened the Onjisay Aki International Climate Summit, an unparalleled opportunity for cross-cultural dialogue on climate change with environmental leaders and Indigenous Knowledge Keepers from 14 Nations around the world. In collaboration with Turtle Lodge, the Prairie Climate Centre was invited to support the documentation and communication of knowledge shared at the Summit. This process of Indigenous-led community-based research took an inter-epistemological approach, using roundtable discussions within a ceremonial context and collaborative written and video methods. The Summit brought forward an understanding of climate change as a symptom of a much larger problem with how colonialism has altered the human condition. The Knowledge Keepers suggested that, in order to effectively address climate change, humanity needs a shift in values and behaviours that ground our collective existence in a balanced relationship with the natural world and its laws. They emphasized that their diverse knowledges and traditions can provide inspiration and guidance for this cultural shift. This underscores the need for a new approach to engaging with Indigenous knowledge in climate research, which acknowledges it not only as a source of environmental observations, but a wealth of values, philosophies, and worldviews which can inform and guide action and research more broadly. In this light, Onjisay Aki makes significant contributions to the literature on Indigenous knowledge on climate change in Canada and internationally, as well as the ways in which this knowledge is gathered, documented, and shared through the leadership of the Knowledge Keepers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10584-021-03000-8.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.055 | 0.027 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".