“For the Gram”: An Exploration of the Conflict between Influencers and Citizen-Consumers in the Public Lands Marketing System
Bibliographic record
Abstract
Capturing memories is integral to public lands visitors’ consumer experiences. Today, social media allows us to share photographs and videos in the public domain, whether it be for instant gratification, economic gain, or both. The phenomenon of sharing public lands digital imagery on social media has created tensions in the public lands marketing system (PLMS) between those wanting to preserve the outdoors and those seeking to monetize it. Using the Instagram account @publiclandshateyou as a case study site, this research utilizes an interpretive “thick data” visual analysis to examine how interlinked marketing systems (e.g., travel, tourism, outdoor recreation), which includes the social media marketing system (SMMS) contribute to this conflict in the PLMS. Findings indicate that citizen-consumer oriented practices, rooted in “sense of place,” attempt to bring change to the interlinked marketing systems.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".