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Record W6979827712

all the little shards

2020· dissertation· en· W6979827712 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePoetryIsolation (microbiology)Perspective (graphical)Cognitive dissonanceLonelinessIdentity (music)WonderVernacular
DOInot available

Abstract

fetched live from OpenAlex

all the little shards is a collection of short personal poems about place, examining the 24 year old speaker’s move from stagnation in semi-rural Ontario to Saskatoon, Saskatchewan. As the speaker explores place and life in a new location without any support systems, he struggles with clinical depression, alcohol, social anxiety and ineptitude, as well as loneliness and dissociation with people and place. Isolation and defamiliarization of places and vernacular permeate the collection, and as the narrative progresses the speaker tries to embrace his new home to create, overcome, and enjoy a distinctly different mentality of place. The use of formal styles of poetry and flâneur motifs help locate the speaker and his thoughts while lyrical free-form pieces bring in pauses for meditation, and questions that change the speaker’s perspective, painting pictures of places scattered across the urban and rural prairie. \nThe collection is a journey through self and place, bookended by uncertainty towards the future and the looming move away from a norm—from Ontario to Saskatchewan, and from Saskatchewan to the uncertain right as things seem to be falling into place. This uncertainty about what was before and what comes after is confronted by the aforementioned questions as places become fluid, and the speaker’s changed perspective creates personal dissonance and new appreciation for Saskatchewan.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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 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
Published2020
Admission routes1
Has abstractyes

Explore more

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