To Conserve and Protect: “Making Sense” of Conservation Officer uses of Emotional Labour
Bibliographic record
Abstract
Given the unique role that conservation officer’s play in our society, it is critical that researchers better understand factors that may influence the activities and behaviours of the individuals tasked with dealing with complex emotions of others, ensuring the safety of Canada’s backcountry wilderness, all while maintaining a tough persona and enforcing the law (Moreto et al., 2015; Moreto, 2016). Hochschild’s (1983) concept of “emotional labour” is employed within this document to explore the extent to which conservation officers rely on their ability to deal with complicated emotions, within themselves and those of individuals they encounter. Due to the limited literature exploring the nexus between conservation officers and emotional labour, a grounded theoretical approach was selected to accommodate the emerging nature of these concepts. Identifying the driving factors in conservation officer behaviour provides avenues to better understand the feasibility, applicability, and likelihood of success when introducing policy aimed at improving officer mental health (Moreto et al., 2015). This study is based on twelve in-depth qualitative interviews and six commentated walks with members from provincial and private parks in British Columbia, Canada. Within it, I will explore how conservation officers engage in emotional labour, as well as its impact. The results reveal how managing emotions according to the organizationally mandated display rules can affect an officer’s well-being, and it highlights the need for future research to enable park enforcement organisations to deal more effectively with work-related stress.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".