Picture-perfect, peaceful, and protected: Canadian national parks’ multimodal discourses and representations of mandates on Instagram
Bibliographic record
Abstract
This thesis analyzed multimodal social media communications of a government agency that manages national parks, to study the kinds of discourses that are constructed by national parks agencies through representations of the parks and their nature. Furthermore, it analyzed and interpreted how the discourses relate to the mandate and other missions that the agency must adhere to, which are generally a combination of focusing on nature conservation while also providing visitation opportunities. I analyzed the content of the official Instagram account of Parks Canada, which manages the country’s national parks and cultural heritage. A theoretical and analytical framework combining Discourse Analysis and Social Semiotics have been applied to perform a Multimodal Critical Discourse Analysis on @parks.canada and specifically on 79 of their posts published in 2019. Through analysis of the Instagram page’s affordances and the visual and linguistic communications within the posts, four main themes have been identified: National parks are perfect holiday destinations, Covert protection of unspoiled nature, National parks as abstract homelands and Drawing a crowd with empty landscapes. The agency mainly uses their Instagram to encourage visitation of the parks, in similar ways as commercial representations of nature, while discussions of protecting the nature’s ecological integrity and (Indigenous) Canadians’ relation with and influence on the parks are minimal. This limited representation of the parks’ purposes and meaning echoes previous studies, where idealized landscapes and the divide between nature and culture were main representations of (protected) natural areas. It is argued that using the @parks.canada page to communicate more regarding the agency’s other missions and the national parks’ purposes and value besides holiday destinations, could be conducive to give the large Instagram audience a broader understanding of the parks and people’s influence on and responsibility regarding nature. (Less)
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".