Why do wildlife matter: transforming interview information into music and video to advance environmental conservation
Bibliographic record
Abstract
As the human population rapidly increases, the pressures on wildlife to adapt to the changing landscape and to coexist with humans is also increasing. Understanding why wildlife matter to humans is critical in helping plan and execute conservation strategies as we move into the future. This research investigates why wildlife matter to a cohort of environmental professionals including retired and practicing wildlife biologists, as well as post-secondary students studying environmental science and studies, and explores the perspectives of Indigenous peoples on why wildlife matter to them and their culture. The qualitative research data was artistically transformed into video and song to showcase the research in diverse media. Study methods include conducting interviews, completing a literature review, as well as creating audio/visual tools to display the results of the data found within the study. This unique research initiative identified several significant findings, to conclude that wildlife matter to biologists, students, and Indigenous peoples for a variety of reasons. For example, personal perspectives on how wildlife play a direct role in their life, as well as the role wildlife play as part of the world’s ecosystems in providing balance and stability to nature’s processes. Inductive coding thematic analysis identified 5 themes within the data of why wildlife matter which includes 1) Ecosystem Importance 2) Resource for Humans 3) Personal Value – Happiness and Wellbeing 4) Recreational Purposes 5) Existential Purposes – Coexistence. The findings of the research were created into a video titled “Why Do Wildlife Matter?” and song “We Decide.” The creative works were awarded the 2020 Conservation Education Award through The Wildlife Society in recognition of outstanding media and audio-visual work which effectively conveys sound conservation concepts to the public. As a songwriter, musician, and wildlife biologist, this multidisciplinary research bridges several disciplines to better understand human motivations toward environmental conservation.
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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".