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Record W4381328060 · doi:10.3828/qs.2023.12

<i>La vague celtique</i> , Cultural Heritage, and the Creative Economy in Rural Southern Québec

2023· article· en· W4381328060 on OpenAlexaboutno aff
Patrick R. Ireland

Bibliographic record

VenueQuebec Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsIrishTourismEthnologyGeographyEconomyHistoryPolitical scienceEconomic historySociologyArchaeologyEconomics

Abstract

fetched live from OpenAlex

As Québec has moved to strengthen its creative economy in recent years, both English-speaking communities and rural areas have become areas of concern. Surprisingly under-examined has been the multiplication of Irish commemorations, memorials, and related heritage attractions in the overwhelmingly French-speaking Appalachian foothills of southern Québec. Irish migration there occurred largely between the 1810s and mid-1840s (before the Great Famine) after the opening of several roads that completed overland connections between Québec City and Boston: the Kennebec Road along the Chaudière River (and the Etchemin Road branching off it) that existed in rudimentary form by 1810; the Craig Road, constructed in 1810; and the Gosford Road, begun in the late 1830s as a winter spur off the Craig. These routes drew Irish migrants into the townships. Their traditions and memories of pioneer life and sectarian tensions have lived on among the area’s dwindling English-speaking communities. Some of their members have rallied to preserve this Irish heritage before it is lost. Bolstered by regional and provincial tourism offices, local businesses, and many francophone descendants of Irish settlers – and also by greater acceptance of pluralism in a more culturally self-confident Québec – they have been building an ecumenical, authentic, and visible Irish presence. So far, they have also avoided the negative aspects of creative-cultural tourism.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.316
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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