From warrior to guardian: An autoethnographic study of how consumers think about and interact with the natural world
Bibliographic record
Abstract
Abstract Consumers are increasingly concerned about how their interactions with the natural world affect both the health of that environment, and their own well‐being and enjoyment of life. More aware consumers seek to make sense of the natural world around them and consider how their consumer behavior impacts this environment. How actors notice and bracket ecologically material cues from a stream of experience and build connections and causal networks between these has been referred to as ecological sensemaking. This research examines ecological sensemaking in a specific context, that being in the experience of catch‐and‐release fishing. Data were gathered through a process of autoethnographic inquiry obtained over the course of four fishing trips. The results reflect the process of ecological sensemaking pertaining to the experience. Through the findings, we propose a new concept, ecological reasoning, which seeks to provide a critical link between ecological sensemaking and ecological embeddedness. Using this new concept, the research contributes to extant understanding of how consumers think about and interact with the natural world. Apart from constructing an overarching narrative of the experience, four subnarratives are also identified, in a chronological sequence that comprises the entire experience of catch‐and‐release fishing. The findings have implications for the broader management and marketing disciplines seeking to establish better ways of interacting with the natural world, both for themselves and their consumers.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".