MétaCan
Menu
Back to cohort
Record W6931683670 · doi:10.5287/ora-yjzpbvvvg

Nuna Nalluyuituq/the land remembers: spatial technology, collaborative community engagement, and capacity building in Southwest Alaskan cultural landscapes

2023· dissertation· en· W6931683670 on OpenAlexaboutno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2023
Typedissertation
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsCultural landscapeTundraVegetation (pathology)PeninsulaExcavationHuman geographyLandscape archaeologyColonialismLand useIndigenous

Abstract

fetched live from OpenAlex

For over a thousand years, the Yup’ik (pl. Yupiit) people have inhabited the vast subarctic wetland tundra of the Yukon-Kuskokwim (Y-K) Delta in southwest Alaska. Despite enduring dire challenges in the colonial era (c.19th century to present) — violence, disease, disenfranchisement, and forced relocations—- they have emerged more resolute and resilient than ever. Modern Yup’ik communities actively reclaim their culture in a number of ways, including working with outsiders: as exemplified by the Quinhagak Archaeological Project’s rescue excavation of the Nunalleq site, a 17th century ancestral village. This doctoral research project seeks to build upon the collaborative research relationship established in Quinhagak, a village of 700 Yupiit on the Bering Sea, by looking beyond the Nunalleq site to the wider cultural landscape— how can spatial technology be used in close collaboration with local communities to protect cultural landscape heritage, and build capacity in the face of catastrophic climate change? In places like Quinhagak, multiple strands of evidence are available for investigation. Yup’ik oral history provides a rich source of traditional knowledge about the cultural landscape. Furthermore, unrecorded archaeological features are hard to detect unaided, but their presence is revealed through subtle indirect clues in the landscape, like vegetation differences. Thus, through ancestral memory and the natural environment, the land remembers the activities of its past inhabitants. In this thesis by papers, five peer-reviewed articles are presented. Article 1 demonstrates the value of Yup’ik oral tradition for characterising cultural landscapes. It also suggests that vegetation signatures associated with archaeological sites can be detected with remotely-sensed multispectral imagery. Article 2 is a spatial analysis of known cultural sites around Quinhagak, illustrating the profound relationship between the community’s past and its two salmon-bearing rivers. Article 3 proves conclusively that archaeological sites can be easily detected though anomalous vegetation patterns in subarctic tundra, and highlights the advantages of imagery captured by Unpiloted Aerial Vehicles (UAV’s) versus satellite-derived ones— image quality, cost effectiveness, and data sovereignty. Article 4 further demonstrates the utility of UAV’s by showing how they can be used to measure erosion severity when used in conjunction with archival satellite imagery. Finally, Article 5 suggests that capacity may be built by automating the processing of spatial data in Google Earth Engine, a free cloud-based remote sensing client. This will saves users dozens of steps, and will facilitate the training of local cultural professionals to tackle the climate crisis and threats to heritage in the Y-K Delta.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.047
GPT teacher head0.295
Teacher spread0.248 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Research Archive (ORA) (University of Oxford)Same topicPrenatal Screening and DiagnosticsFrench-language works237,207