Reassembling transboundary natural resource governance: Case studies of posthuman wild food systems in Eeyou Istchee
Bibliographic record
Abstract
The governance of natural resources is often characterised by ecological, social, and jurisdictional complexity. One dimension of this complexity is epistemological and ontological pluralism, wherein governance structures and processes must contend with multiple knowledge systems and worldviews. In Canada, for example, pluralistic natural resource governance often includes Indigenous Peoples, in addition to government, non-governmental, and industry actors. Even in cases where these diverse governance actors share broadly agreed-upon conservation and sustainable development goals, pluralism can have significant implications for collective action, as well as epistemic justice and reconciliation. While different knowledge systems and worldviews can enhance understanding, inclusion, and innovation, they can also create frictions and controversies. Indigenous knowledge systems are frequently incommensurable with the dominant scientific and bureaucratic knowledge systems of natural resource governance. This is particularly evident in cases involving the social roles of nonhuman actors, which often play active parts in Indigenous ontologies and epistemologies. In this dissertation, I first synthesise the literature on actor-network theory (ANT), and analyse how this approach to understanding human/nonhuman networks could also inform understandings of knowledge weaving and pluralistic natural resource governance. To demonstrate empirically how knowledge weaving can challenge network governance approaches, I conduct a case study of moose and forestry governance in Eeyou Istchee, the James Bay Cree Territory of northern Quebec, Canada. Cree livelihoods are closely linked to wild food species like moose. However, these species are being heavily impacted by forestry and other resource development. Fuzzy cognitive mapping was conducted with Cree land-users to explore the different social-ecological impacts to moose habitat. The case study shows that, while some differing Cree and scientific understandings of boundary spanning factors are relatively easy to reconcile, some factors, especially those related to specific local culture and belief, are not. A second case study of lake sturgeon governance in the Cree community of Nemaska demonstrates how an ANT-inspired approach to network governance could help span boundaries between pluralistic governance actors. Like moose, lake sturgeon is an important wild food species being impacted by resource development. Using interviews and participant observation, I describe lake sturgeon actor-networks in Nemaska, identifying the relational networks of humans and nonhumans that influence governance. Through the tracing of these networks, boundary-spanning roles that may be hidden from other approaches are highlighted. Natural resource governance regularly depends on complex relationships and consensus between local land-users, scientists, and policy makers. In such pluralistic settings, shared understandings can be challenging to develop, and I conclude that ANT and a wider turn towards posthumanism would help decentralise the human in network governance methods, thereby creating novel insights for overcoming conflicts and improving collective action
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".