Can Blockchain Lead us to Forest Sovereignty? \nA future imagining of more-than-human relations.
Bibliographic record
Abstract
This work endeavoured on a preliminary futuristic imagining of what it would take for Forest to practice its sovereignty in our current system. The concept of Forest Sovereignty is employed to mitigate against the challenges identified in this work within the forest governance space in Canada. As an attempt to make this imagining real, the practice of governance is leveraged to anchor the concept of Forest Sovereignty. Because there are signals in the Web 3.0 space that are exploring ways in which natural resources, particularly Forest, may be governed differently, blockchain technology is explored as a tool to do this. While it is determined that blockchain technology is not sufficient, it is revealed that this research question is essentially an exploration into two significant matters: 1) understanding that this question is essentially exploring the relationship between a natural entity (forest) and a piece of technology (blockchain), and 2) ways in which we, humans, may be able to engage more-than-human beings in meaningful ways as an attempt to shift away from human-centric systems. This shift is considered vital as human beings continue to demonstrate a lack of regard towards Earth. \n \nKeywords: Forest Governance, Blockchain, Non-Human Relations, Web 3.0, Natural and Tech System Mergers
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 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".