Bordering on crisis: A qualitative analysis of focus group, social media, and news media perspectives on the Republic of Ireland-Northern Ireland border during the ‘first wave’ of the COVID-19 pandemic
Bibliographic record
Abstract
RATIONALE: International border controls were among the earliest and most effective of measures to constrain transmission of COVID-19. However, such measures are complex when established borders are open yet politically contested, as for the border that divides the Republic of Ireland (ROI) from Northern Ireland (NI). Understanding how this border affected the everyday lives of both populations during the pandemic is important for informing the continued development of effective responses to COVID-19 and future health crises. OBJECTIVE: This multi-methods study aimed to explore public perspectives on how the ROI-NI border affected experiences of and responses to the 'first wave' of the pandemic. METHOD: The study collated data from focus groups (n = 8), news articles (n = 967), and Twitter posts (n = 356) on the island of Ireland, which mentioned the ROI-NI border in relation to COVID-19. Thematic analysis was used to explore the range of perspectives on the role played by the border during the early months of the pandemic. RESULTS: Analysis identified three themes: Cross-Border Interdependencies illustrated the complexity and challenges of living near the border; Interpretations of Cross-Border Policy Disparities showed that lay publics perceived NI and ROI policy approaches as discordant and politicised; and Responses to Cross-Border Policy Disparities revealed alternating calls to either strengthen border controls, or pursue a unified all-island approach. CONCLUSIONS: Results reveal clear public appetite for greater synchronisation of cross-border pandemic responses, emphasise the specific vulnerability of communities living near the border, and highlight the risk of long-term socio-political repercussions of border management decisions taken during the pandemic. Findings will inform implementation of pandemic responses and public health policies in jurisdictions that share a porous land border.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".