Seeing things differently: How are environmental conditions perceived and why does it matter?
Bibliographic record
Abstract
Parks and protected areas that provide recreational opportunities for visitors are often faced with a set of unique management challenges. Primarily, this includes balancing the preservation of the ecosystem with recreational use, often involving the mitigation of visitor behaviors. As well, various groups that may interact with these areas often have conflicting priorities for or opinions on management actions. In order to promote sustainable visitor behaviors, increase support for management initiatives, and address some of these conflicting opinions, an understanding of how environmental conditions are perceived among user groups is needed. Therefore, this study sought to illuminate how two groups that differ in their levels of experience and knowledge with respect to a protected area with high levels of visitation perceive the state of its environment. A survey was administered to people identified as “experts” on the Niagara Glen Nature Reserve (Ontario, Canada) as well as to those identified as more casual “visitors” to the reserve. Perceptions of ecological conditions are compared to empirical measurements. For both visitors and experts, the overall perceptions of environmental conditions differed significantly from the ecological data, with visitors generally providing higher ratings of ecosystem conditions, whereas experts generally provided lower ones. Visitors and experts also differed significantly from one another in their perceptions—a meaningful finding for understanding intergroup conflicts as well as the basis for support for management initiatives. The findings highlight the importance of considering perceptions of environmental conditions between groups, and of understanding how perceptions relate to measured ecological data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".