Capturing the Muslim experience with airport staff, security checkpoints and surveillance systems at Dublin airport.
Bibliographic record
Abstract
Recent research has shown that the airport can be an unwelcoming and hostile place for Muslims (Blackwood et al., 2013; Bonino, 2015; Nagra & Maurutto, 2016; Selod, 2019). With the drastic increase in the implementation of security and surveillance measures at airports since the 9/11 attacks, studies have shown these measures can affect various populations differently (Blackwood et al., 2015; Nagra & Maurutto, 2016). While studies have examined the Muslim experience at airports in Scotland, Canada and America, no studies have investigated the Muslim experiences at Irish airports. To address the gap, this study set out to examine the encounters between Muslims (living in Ireland) and airport staff, security checkpoints and surveillance systems at Dublin airport. A digital self-completion survey provided quantitative data from 31 individuals. This survey asked participants to provide their views, opinions and encounters with security personnel, security checkpoints, monitoring technologies and profiling at Dublin airport. A thematic analysis was used to interpret the findings from the survey. The findings from this study show that participants generally reported having an overall positive experience at Dublin airport. However, some individuals recalled having a negative experience with additional screening procedures in particular at Dublin airport as participants described these practices as an embarrassing and uncomfortable experience. More than half of respondents (61.29%) also believed that profiling happens at airports. The impact of these results will be compared with existing current literature. This study highlights the importance of raising awareness to the issue in an Irish context as the Muslim community is growing in Ireland. The findings presented in this thesis will add to our understanding of the Muslim experience at airports in an Irish context. This study should, therefore, be of value to researchers wishing to gain further insight into the issue in Ireland.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".