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Record W2119833677 · doi:10.1080/1743873x.2012.669765

Seeking roots and tracing lineages: constructing a framework of reference for roots and genealogical tourism

2012· article· en· W2119833677 on OpenAlexaff
Gregory Higginbotham

Bibliographic record

VenueJournal of Heritage Tourism · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsBrock University
Fundersnot available
KeywordsTourismScholarshipIdentity (music)SociologyConstruct (python library)BelongingnessConceptual frameworkHeritage tourismHospitalityTourism geographyEpistemologySocial scienceGeographyAestheticsPolitical scienceSocial psychologyPsychologyArchaeology

Abstract

fetched live from OpenAlex

Travel for the purpose of seeking roots, or roots tourism, is understood to be focused on the descendants of a diaspora living in contemporary multicultural societies and travelling to ancestral homelands in search of identity and belongingness. It is an almost negligible niche segment of heritage tourism due to an obscure amalgam of contextual concepts. The primary purpose of this review is to construct a conceptual framework of reference based on sociological and psychological literatures concerning identity and belongingness. This framework is then employed to synthesize contributions to roots tourism from scholars both within and beyond tourism studies. On the basis of a unique conceptual overlap with seeking roots, travel for the purpose of tracing lineages, or genealogical tourism, by diasporic descendants is concisely discussed with respect to tourism scholarship. It is recommended that researchers in tourism studies systematically develop the contributions of this review by continuing to draw from established disciplines and closely aligned fields of study.

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)0.009
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.005
Science and technology studies0.0060.031
Scholarly communication0.0090.012
Open science0.0030.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.352
Teacher spread0.299 · 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 designTheoretical or conceptual
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

Citations40
Published2012
Admission routes1
Has abstractyes

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