Positive Public Communication Tools for Enhancing Wildlife-Road Safety in the Chebucto Peninsula Region, Halifax, Canada
Bibliographic record
Abstract
Wildlife-vehicle collisions occur frequently and are hazardous for both humans and animals involved (Huijser, et al., 2016; Fudge, Freedman, Crowell, Nette & Power, 2007; Ramp, Wilson & Croft, 2016). Urban (and other) developments are leading to habitat fragmentation, which results in the displacement of many animal species (Beazley, Snaith, MacKinnon & Colville, 2004; Fudge, et al., 2007; Perkl, et al., 2018). Fragmentation can also lead to more wildlife-road interactions, increasing the potential for collisions to occur (Fudge, et al., 2007). Human-wildlife conflict can also be related to issues regarding wildlife protection around roads and can result in negative attitudes and behaviors toward wildlife (Frank, 2015; Ramp, et al., 2016). It is important to engage the public about issues related to wildlife and roads to increase awareness and encourage human-wildlife coexistence. Futerra Sustainability Communications (2010) has determined that the best way to engage public audiences in support of biodiversity is by incorporating a “love” component and an “action” component to public communication strategies, for audiences not already invested in the issue. By employing a “Love + Action” framework (Futerra Sustainability Communications, 2010), this study develops guidelines and appropriate messages for good practices for positive public communication on the topic of wildlife protection around roads. Application of the guidelines and important messages are demonstrated through the creation of a visual communication tool (i.e., infographic) for wildlife protection around roads in the Chebucto Peninsula, which is an important area for wildlife connectivity (Halifax Regional Municipality & O2 Planning & Design, 2018). The guidelines, important messages and infographic are intended for wildlife protection around roads, but could be transferable to producing positive public communication tools for other wildlife/environmental issues.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".