Preliminary Insights into Science Communication Strategies in Canadian Mining Messaging: A Mixed-Methods Perspective
Bibliographic record
Abstract
This study investigates how the mining industry employs science communication tactics, specifically framing, warmth, honesty, and relatability when engaging with the public. Guided by three primary research questions, this project seeks to identify: (1) which frames and tactics Canadian mining organizations employ when communicating about mining, (2) how these tactics influence engagement among audiences with pro-, anti-, and neutral attitudes toward mining, and (3) whether the use of tactics varies based on the type of organization.A mixed-methods approach integrates content analysis, survey responses, and thematic analysis. Advertisements, corporate websites, and corporate responsibility documents from various mining organizations are systematically coded to identify framing strategies and communication techniques. To evaluate changes in public perceptions, knowledge, and behaviours, participants complete pre-engagement surveys to establish baseline attitudes toward mining. They then engage with assigned materials in two stages: first independently and later through guided discussion and interviews conducted via Zoom. Post-engagement surveys capture immediate reactions and subsequent changes in perception, knowledge, and potential actions. Transcribed interviews from guided discussions are analyzed thematically to uncover deeper insights into how audiences engage with mining-related messaging.This research is significant for its focus on the intersection of industry messaging and public engagement, addressing a critical gap in understanding how science communication influences public trust and opinion in resource-driven sectors. Insights from this study will inform best practices for transparent, relatable, and effective communication in the mining industry, with broader implications for improving public engagement strategies in other science-based fields.
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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 | 0.059 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".