Expanding the role of social science in conservation through an engagement with philosophy, methodology, and methods
Bibliographic record
Abstract
Abstract The Special Feature led by Sutherland, Dicks, Everard, and Geneletti ( Methods Ecology and Evolution , 9, 7–9, 2018) sought to highlight the importance of “qualitative methods” for conservation. The intention is welcome, and the collection makes many important contributions. Yet, the articles presented a limited perspective on the field, with a focus on objectivist and instrumental methods, omitting discussion of some broader philosophical and methodological considerations crucial to social science research. Consequently, the Special Feature risks narrowing the scope of social science research and, potentially, reducing its quality and usefulness. In this article, we seek to build on the strengths of the articles of the Special Feature by drawing in a discussion on social science research philosophy, methodology, and methods. We start with a brief discussion on the value of thinking about data as being qualitative (i.e., text, image, or numeric) or quantitative (i.e., numeric), not methods or research . Thinking about methods as qualitative can obscure many important aspects of research design by implying that “qualitative methods” somehow embody a particular set of assumptions or principles. Researchers can bring similar, or very different, sets of assumptions to their research design, irrespective of whether they collect qualitative or quantitative data. We clarify broad concepts, including philosophy, methodology, and methods, explaining their role in social science research design. Doing so provides us with an opportunity to examine some of the terms used across the articles of the Special Feature (e.g., bias), revealing that they are used in ways that could be interpreted as being inconsistent with their use in a number of applications of social science. We provide worked examples of how social science research can be designed to collect qualitative data that not only understands decision‐making processes, but also the unique social–ecological contexts in which it takes place. These examples demonstrate the importance of coherence between philosophy, methodology, and methods in research design, and the importance of reflexivity throughout the research process. We conclude with encouragement for conservation social scientists to explore a wider range of qualitative research approaches, providing guidance for the selection and application of social science methods for ecology and conservation.
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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 | 0.236 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.033 | 0.027 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.010 | 0.017 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".