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Record W2985461965 · doi:10.1007/s10761-019-00521-y

Migration and Memorials: Irish Cultural Identity in Early Nineteenth-Century Lowell, Massachusetts

2019· article· en· W2985461965 on OpenAlexfundno aff
Colm Donnelly, Eileen Murphy, Dave McKean, Lynne McKerr

Bibliographic record

VenueInternational Journal of Historical Archaeology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastUniversity of Massachusetts
KeywordsIrishEthnic groupHistoryYardIrish seaIdentity (music)Historical archaeologyImmigrationEthnologyIndustrial RevolutionArchaeologyAncient historySociologyAnthropologyArtAesthetics

Abstract

fetched live from OpenAlex

Abstract Lowell is considered as the birthplace of the industrial revolution in the early nineteenth-century United States. Originating in 1822, the new textile factories harnessed the waters of the Merrimack River using a system of canals, dug and maintained by laborers. While this work employed many local Yankees, it also attracted groups of emigrant Irish workers. Grave memorials are a valuable source of information concerning religious and ethnic identity and an analysis of the slate headstones contained within Yard One of St Patrick’s Cemetery, opened in 1832, provides insight into the mindset of this migrant community. The headstones evolved from contemporary Yankee memorials but incorporated Roman Catholic imagery, while the inclusion of shamrocks and details of place of origin on certain memorials attests to a strong sense of Irish identity. The blatant display of such features at a time of ethnic and religious sectarian tensions in Massachusetts demonstrates the confidence that the Irish had of their place in the new industrial town.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.593
Threshold uncertainty score0.819

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.318
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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