Using an Ecological Metaphor to Build Adaptive and Resilient Research Practices
Bibliographic record
Abstract
In this paper, we reflect on our efforts to undertake qualitative research in our investigations of the geographies of Aboriginal people and of rural communities across Canada. In particular, we consider the ecological metaphor to describe the nature of power relations between researched and researcher in the production of situated knowledges. Gillian Rose, in her influential 1997 article, ‘Situating knowledges: Positionality, reflexivities and other tactics’, suggested that these relations might be conceptualised as ecological, characterised by fluid connections among researcher, researched and text, marked by fragmented understandings and uncertainty throughout research and distribution practices. Although not fully developed as a metaphor in her work, Rose sees the relationship as potentially risky for the researcher and for the research subjects and thereby draws a fairly pessimistic conclusion about the outcomes of the research relationship. We have been inspired by this notion and seek to develop the ecological metaphor in this article by drawing on contemporary systems ecology that emphasises the uncertainty and surprise inherent in ecological-social systems. In contrast to Rose’s pessimism, we suggest that research practices can be designed to embrace the uncertainty and partiality of knowledge creation as well as the dynamism of the research process by methods that are adaptive and resilient. We suggest that such a position has implications for four elements of our research: preparing for surprise, how we involve research participants, how we consider our roles as researchers, and how we define research success. We interrogate our own research experiences to develop this framework and to identify challenges of putting it into practice.
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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 | 0.076 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.018 | 0.133 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.008 |
| 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".