Online conversations among Ontario university students: Environmental concerns
Bibliographic record
Abstract
As the 'next generation' guardians of the environment, there appears to be limited inquiry into young Canadians' environmental concerns. At the same time, online social networking is a predominant method of communication among young adults. This research explored online conversations regarding environmental concerns among young Canadian adults targeting the university student population. A qualitative content analysis was conducted using posted conversations from the online social media network Facebook. Conversations addressing environmental issues were summarized into four major themes. The first theme, 'Built Environment' (127 postings) centred on housing and transportation. The second theme, 'Natural Environment' (55 postings) accounted for issues of air quality, pollution and water quality. The third theme, 'Environmental Restoration' (52 postings) highlighted young Canadian adults' plans for environmental recovery. The fourth theme, 'Engagement and Activism' (31 postings) underscored students' use of the online social networking site for environmental advocacy. Young adults appeared to be environmentally conscious and, through the use of social networking, exchanged knowledge and opinions, and advocated for environmental change. Online social networking sites, such as Facebook, can serve as a communication channel that facilitates health information sharing and more importantly cultivates community capacity focused on environmental health promotion among young adult users.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".