Seeing beyond the frames we inherit: A challenge to tenacious conservation narratives
Bibliographic record
Abstract
Abstract Natural and social scientists everywhere are struggling to understand how to proceed in the face of continued biodiversity loss and the injustices brought upon people living in and around conservation landscapes. This has resulted in increasing calls for critical reflection on the narratives driving conservation research and practice. Narratives can be understood as part of a larger process of “framing” within an intellectual community, which includes the way studies are defined and discussed. Identifying, reflecting on and even destabilizing entrenched frames can be helpful for understanding when and where our diagnosis or understanding of a problem fails. However, we also need to understand the scholarly processes that create and reify some frames (and not others) over time. We address these needs by developing a mixed‐method approach that integrates qualitative frame analysis and quantitative science mapping to identify the origins of the dominant frame and trace its reproduction in the scientific literature over time. We demonstrate this approach using the case of the Bale Mountains, an internationally recognised centre of species endemism in Ethiopia. Our results show the enduring influence of the perceptions and values of a few early conservation scientists working with limited data. This led to erroneous assumptions and conclusions that, in some cases, were corrected by later research, but in many cases were not. This was a function of the social and intellectual structure of the scientific network, minor but consequential decisions in data interpretation and specific citational habits. Synthesizing these results, we identify several linked mechanisms that helped the dominant frame retain its tenacity and may also be at work in other contexts. We close with a discussion on how others might apply our approach and how future scientific research and conservation practice could proceed differently. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.132 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.029 | 0.100 |
| Scholarly communication | 0.038 | 0.058 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".