Perceptions of risk to water environments in the lower Fraser basin, British Columbia
Bibliographic record
Abstract
The following thesis presents the results of a two year study that addressed lay perceptions of the risks to the water resources of the Lower Fraser Basin, British Columbia. Studies of this nature are important because by clarifying public perceptions, risk communication policies as well as land and water resource use plans that accommodate people's concerns can be developed appropriately. The study was based on a written survey that was administered to 183 lay subjects in four communities within the Lower Fraser Basin. Sixteen experts participated in a portion of the survey. Analysis involved the determination of how people perceive distinct human activities in terms of the risk each may pose to the health of aquatic ecosystems. Further analysis compared these perceptions to those of specialists in the aquatic sciences. The thesis is structured as a collection of three papers that examine different aspects of the study. The First paper provides a review of the overall data set, while the remaining two papers address related subsets of the data. One paper reviews perceptions of activities that cause eutrophication problems and the other reviews perceptions of forest industry activities. The results demonstrated that people tend to view risks to water environments in a multi-faceted fashion. Four factors were found to influence people's general perception of risk and the need for regulation of the activities that were perceived to pose the risk. These factors were termed Ecological Impact, Human Benefit, Controllability and Knowledge. Another significant result was the fact that there were often striking differences between the views of the lay sample and those of the expert sample. Some activities were perceived by the lay sample as posing substantial risk while the experts did not view this to be the case. Alternatively, for some activities, the reverse scenario occurred. Accordingly, there were differences in judgements between the two groups as to the degree of regulation that should be imposed on the respective activities.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".