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Record W7014877208

Review of <i>Buffalo Jump: A Woman's Travels</i> By RitaMoir

2001· article· en· W7014877208 on OpenAlexaff

Bibliographic record

VenueLincoln (University of Nebraska) · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican and British Literature Analysis
Canadian institutionsSaskatchewan Ministry of Agriculture
Fundersnot available
KeywordsPraiseMetaphorPower (physics)Set (abstract data type)Face (sociological concept)Shadow (psychology)CourageMeaning (existential)SuspectSketch
DOInot available

Abstract

fetched live from OpenAlex

Buffalo Jump is a surprisingly good little book. I say "surprising" because it's such an unassuming, small paperback, as if its publisher didn't expect much of it; as a result, the reader may expect the same. Thankfully, I was wrong. It's a memoir, a travel document, a story about families, and a story about stories. It's a song of praise for the writer's mother and grandmother- in fact, for prairie women in general and the hardships of their lives in those generations. Unfortunately, it loses power because it tries to do so much, while nevertheless managing to be nearly always interesting. Rita Moir spent many years as a journalist, and the anecdote, the scene, is where she works best, cutting and sharpening each incident until she achieves an intensity that causes the reader to lose herself in it. She has wanted to write beautifully, lyrically, I would say, but those many passages where she's tried to rise above the facts and the "story" were, for this reader, merely annoying in their lack of concreteness. I never re8:lly "bought" the buffalo metaphor she uses either, chiefly because it felt so wrong for my understanding of women's nature, but which is surely her point: that women, too, might have an affinity with big, powerful beasts. (She points out that a female always led the herd.) In telling women's stories she has set herself the directive of never allowing her characters to see themselves or be seen as victims, but to emphasise instead their courage and self-reliance in difficult and unfair situations. In doing this she has preferred not to dig too deeply into their emotions, into their real feelings- those of conflict and desire and despair as well as joy-and I was left with a sense of a clever, gutsy, occasionally dreamlike drifting over the surface of experience, avoiding the real pain (except for the story about her sister's cruel treatment by medical people where Moir reveals her rage at her sister's suffering, which also becomes real to the reader). And yet despite Moir's avoidance of the "victim" narrative, it is there anyway, between the lines: the bloody unfairness of so much of women's lives, and the hopelessness of finding an adequate response in a society where everything is stacked against them. The narrative seemed to me riddled with the very pain she refuses to express directly. Moir has written a compelling, for the most part cheerful memoir, in which she determinedly shuns the traditional line about women's lives, desires, and experiences. I applaud her for that, and for her courage, while wishing she might have found a way within that ethic that did not exclude a greater intimacy with the souls of her characters. If she had added that, this book, with its many strengths, would have gone far.

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)0.002
metaresearch head score (Gemma)0.013
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: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.007
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0210.012

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 head0.008
GPT teacher head0.176
Teacher spread0.168 · 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
GenreReview

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
Published2001
Admission routes1
Has abstractyes

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