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Record W7053506567

Two Hundred Wildflowers: Annora Brown’s Botanical Watercolours, Scientific Illustration, and Conservationism in Alberta

2017· dissertation· en· W7053506567 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionTSG101SubpoenaHyporeflexiaGloomDemotion
DOInot available

Abstract

fetched live from OpenAlex

Between 1958 and 1960, artist Annora Brown conceived and compiled over two hundred botanical watercolours for the Glenbow Museum. Brown created a body of work that was documentary in nature but also imbued with personal meaning and aesthetic appeal. Her distinctive approach to botanical illustration challenges how scientific information is categorized and communicated in Western culture, and reflects shifting perceptions of the Alberta environment in the twentieth century. The commission was inspired by the publication of Old Man’s Garden (1954), a book written by Brown to fill a gap in the understanding of plants indigenous to the prairie and foothill regions. The book was exceptional in its incorporation of the practical knowledge and lore of southern Alberta First Nation’s communities (specifically Treaty Seven Nations the Piikani and Kainai) with the accounts of early European explorers, Western folklore, literature, and scientific information. This thesis connects Brown’s approach to communicating botanical information in Old Man’s Garden with the artwork she produced for the commission, and analyzes how both projects subvert traditional models of scientific text and illustration. I contextualize Brown’s work within larger frameworks of botanical study in Canada and the early conservation movement in Alberta, and reflect upon how the commission speaks to the connection between landscape and identity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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