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Record W1764122765

This is where I live, but it's not my home : archaeology and identity in Sandwich Bay, Labrador

2008· dissertation· en· W1764122765 on OpenAlexaboutno aff
Jessica Pace

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2008
Typedissertation
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsMetisGeographyContext (archaeology)ArchaeologyIdentity (music)NarrativeHuman settlementGenealogyEthnologyHistoryArtAestheticsLiteratureDatabase
DOInot available

Abstract

fetched live from OpenAlex

This research uses narratives gathered from Métis elders and data from archaeological survey to access information about the importance of abandoned traditional sites near Cartwright, Labrador to the formation and maintenance of southeastern Labrador Métis identity. The correlation between landscapes and the formation of personal and group identity is well documented in the literature concerning landscape archaeology; however, displacement is often overlooked in this context. This research tests theories related to archaeologies of landscape and memory by investigating the ways in which events that have caused displacement of the Labrador Métis from traditional villages to larger, more permanent settlements have influenced and continue to affect the formation of the Métis cultural identity. By considering the interrelated theories of landscape, memory and identity this research demonstrates that landscape not only shapes Labrador Métis group identity but is also intentionally modified by the Métis in an effort to maintain and solidify their connection to their collective past.

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 score0.744
Threshold uncertainty score0.509

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 studies0.0070.004
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.037
GPT teacher head0.338
Teacher spread0.301 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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