“When your participants cannot speak”: Exploring the potential for participatory mapping to represent the geographies of more‐than‐human actors
Bibliographic record
Abstract
Abstract This study examines the use of participatory mapping as a method to represent the values of non‐human entities (more‐than‐humans) in socio‐ecological landscapes. This research seeks to bridge the gap between theoretical developments in more‐than‐human geographies and their practical landscape‐level applications. Through a combination of literature review, as well as participatory mapping interviews and focus groups conducted with 28 participants along Mission Creek in British Columbia, this research explores if and how human proxies can represent the interests of non‐human beings through space. The findings reveal several difficulties in translating more‐than‐human values into mappable spatial data, as well as ethical tensions in balancing human and more‐than‐human priorities for landscapes. We find that there is value in integrating diverse perspectives, such as Indigenous knowledge, citizen science, and children's observations, which contribute nuanced insights into species’ needs, habitat relationships, and ecological processes. Lastly, we also find that participatory mapping may encourage reflexive and relational socio‐ecological thinking, which may promote more inclusive and sustainable environmental decision making. This research contributes to the development of more‐than‐human geography by providing practical insights into the methodological and ethical complexities of representing more‐than‐humans and may also advance efforts toward identifying methods to encourage sustainable and equitable landscape management.
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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 | 0.123 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.042 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| 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".