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Record W4390997553 · doi:10.51644/9781771125468

Scratching River

2022· book· en· W4390997553 on OpenAlexaboutno aff
Michelle Porter

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScratchingGeologyEnvironmental scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Scratching River braids the voices of mother, brother, sister, ancestor, and river to create a story about environmental, personal, and collective healing. This memoir revolves around a search for home for the author’s older brother, who is both autistic and schizophrenic, and an unexpected emotional journey that led to acceptance, understanding and, ultimately, reconciliation. Michelle Porter brings together the oral history of a Métis ancestor, studies of river morphology, and news clippings about abuse her older brother endured at a rural Alberta group home to tell a tale about love, survival, and hope. This book is a voice in your ear, urging you to explore your own braided histories and relationships.

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)0.000
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 score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.005
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0230.007

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 head0.127
GPT teacher head0.214
Teacher spread0.087 · 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
Published2022
Admission routes1
Has abstractyes

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