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Record W2884801647 · doi:10.26522/ssj.v12i1.1740

Without Apology: Writings on Abortion in Canada (Book Review)

2018· article· en· W2884801647 on OpenAlexaffvenueabout
Rebecca Scott Yoshizawa

Bibliographic record

VenueStudies in Social Justice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsAbortionPolitical scienceLaw and economicsSociologyPregnancyBiology

Abstract

fetched live from OpenAlex

In reading Without Apology: Writings on Abortion in Canada, edited by Shannon Stettner (2016), it becomes clear that abortion experiences resonate broadly both socially and politically as much as they are also intimate to interpersonal relations and individual subjectivities and embodiments.Voices from different stakeholders and communities relay violence, oppression, pain, confusion, resolve, and even joy.Such voices are not often spoken loudly, let alone heard.To that end, Stettner collected academic and non-academic autoethnographic voices on abortion.This was a challenge, considering the systematic silencing of those who have had abortions.In the introduction, Stettner discusses her objectives of destigmatizing and normalizing talk about abortion through foregrounding the voices of those who have experienced them.She identifies the subtle perpetuation of stigma, even among those who identify as pro-choice, hinged on a powerful argument that it is predominantly structural factors, and not personal decisions, that give rise to the conditions of reproduction (see MacQuarrie's essay).Stettner suggests that we can see the discourse of "choice" as a kind of strategic bargain with a society that would otherwise not be amenable to talking about abortion.However, this discourse can be politicized following, as Stettner argues, activists and scholars who have moved instead to talking about reproductive justice.This movement is young in Canada and sets the scene for the collection.It is difficult to read this book.It is heartbreaking and enraging.For example, stories in Part 1 that impact the reader with force and weight

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.739
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.357
Teacher spread0.321 · 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.

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
Published2018
Admission routes3
Has abstractyes

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