Bibliographic record
Abstract
There are over 70 million people of Irish descent scattered across the globe, forming a nostalgic Diasporic network that links County Mayo and the rest of Ireland to places such as the USA, UK, Canada, Australia, New Zealand and Argentina. While the concepts of cultural affinity and roots have served as ‘puli’ factors which have attracted large numbers of sentimental ethnic tourists to Ireland since the 1970s, the advent of the Internet as a ‘virtual’ research tool for family history research, and the ongoing reconfiguration of the tourism industry in general, have raised countless questions about the future of Ireland’s genealogy tourism industry. Using evidence gleaned from extensive fieldwork in County Mayo, this study is concerned with the challenges and opportunities currently facing genealogy as a tourism product. Special attention is paid to the notion that tourism in the new Europe needs to be proactively managed with forethought rather than retrospectively reacting to change. Following an overview of the current heritage management difficulties that exist in promoting and encouraging genealogy tourism in County Mayo, an attempt is made to illustrate how these may be best addressed by the development of new marketing strategies and work/organisational practices for the genealogy tourism industry as a whole - including the encouragement of dynamic activity within both ‘real’ and ‘virtual’ tourism sub-spaces. The findings reveal that although there have been significant improvements in the provision of genealogy services in County Mayo since the turn of the millennium, these have not been matched by dedicated marketing strategies by the service providers and the tourism agencies to promote these services. Changes in tourism patterns and the emergence of cultural tourism as an important element in the tourism product mix, suggest that there are real opportunities to further nurture and develop genealogy tourism in County Mayo.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".