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Record W6997277947

Understanding ləkʷəŋən soils: The foundation of environmental stewardship in coastal anthropogenic prairies

2022· dissertation· en· W6997277947 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsStewardship (theology)IndigenousTraditional knowledgeEnvironmental stewardshipExcavationFoundation (evidence)Ethnography
DOInot available

Abstract

fetched live from OpenAlex

Long-term human habitation introduces morphological and chemical changes to soil as a result of cultural, economic, and stewardship practices. These cultural soils, or Anthrosols, are recognized globally. On the Northwest Coast of North America, Indigenous marine and terrestrial land stewardship practices are recognized on present-day landscapes. Increased awareness of these stewardship practices is informed by Indigenous knowledge, ecological legacies, ethnographic studies, and archaeological evidence. This research was undertaken to better understand how lək̓ʷəŋən (Straits Salish) stewardship of a cultural landscape affected the development of soil across a village-garden gradient. On Vancouver Island, British Columbia, Indigenous cultivation of culturally important root foods was interrupted by colonization and its pervasive effects, so an additional research aim was to investigate how cultural soils remain after being disconnected from traditional stewardship. There is a growing global understanding that Indigenous management of ecosystems plays a key role in ecological health. At the regional scale, Songhees First Nation are interested in learning about their soils to inform future restoration efforts and connect youth with their land and culture. The lək̓ʷəŋən Ethnoecology and Archaeology Project (LEAP) is a collaborative research project with the Songhees First Nation to learn more about the physical remains of lək̓ʷəŋən stewardship: soils are a key part of the project. Community knowledge, ethnographic sources, and ecological legacies informed the archaeological excavation and soil sampling in this research. Archaeological excavation was utilized to understand the pedologic and archaeological setting of the site. Soil samples were analyzed for physical and chemical properties to see if a statistical difference between on and off-site samples could be detected. Data from the archaeological excavation were recorded and interpreted. A gradient of influence does exist across the village-garden; the village has a strong physical and chemical signature that can be seen through archaeological excavation, macroscopic remains in the soil, and elevated levels of phosphorous, calcium, and soil pH. Results from the garden are less clear, previous ecological studies and archaeological surveys show evidence of lək̓ʷəŋən stewardship—culturally important plant species and burial cairns are present. However, within the soil, the macroscopic remains and soil chemistry signatures are not as strong as the village which indicates that the health of lək̓ʷəŋən gardens facilitates their continued ecological functioning which ultimately may obscure earlier soil signatures of stewardship. Archaeological investigation alone does not always show the full scope of Indigenous terrestrial management practices. Incorporating present-day community knowledge, ecological legacies of plant cultivation, and utilizing soil chemical data are important to understanding the interconnections between people and their environments across cultural landscapes. Current work on the ecological legacies of plant cultivation can be assisted by investigating the soil as a site that also undergoes co-development with Indigenous stewardship.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.011
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.041
GPT teacher head0.256
Teacher spread0.215 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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