Instagram as a Tool to Counter the Image of Countries as Unsafe: the Case of #LiveLoveLebanon
Bibliographic record
Abstract
The emergence of social media has created new means of intercultural engagement. On Instagram, there is a growing trend of travel pages and travel bloggers whose aim is to introduce and share the highlights of the destinations they travel to. Locals in these destinations also wish to portray their country positively and promote it as a tourist destination, particularly in certain countries of the Middle East where there is the added challenge of an ‘unsafe’ image to combat. This research focuses on Lebanon to find out to what extent Instagram can be considered a means to this end, and if users who come across depictions of Lebanon on Instagram perceive the country as a potential tourist destination. The study draws on Said’s conception of the ‘other’ (1978), Hall’s system of representations (1980) and Pieterse’s hybridization paradigm (1996), and it used a mixed methods approach combining surveys and semi-structured interviews with Canadian participants. Findings broadly show that while Instagram can effectively be considered a tool to counter the ‘unsafe’ image of Lebanon, and while the country may be branded as a potential tourist destination to users who come across favorable depictions of it, algorithm restrictions limit the potential for such contents to fulfill their potential as they do not always reach users who perceive Lebanon to be an ‘unsafe’ place.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".