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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. The 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 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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.187
Threshold uncertainty score0.372

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 studies0.0080.004
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0330.006

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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