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Record W2290596308 · doi:10.14288/1.0052300

Negotiating Surrogacy: The Dilemma of Foster Parents

2017· article· en· W2290596308 on OpenAlexaboutno aff
Veronica Strong‐Boag

Bibliographic record

VenuecIRcle (University of British Columbia) · 2017
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsDilemmaNegotiationFoster parentsPsychologyPolitical scienceSocial psychologyCriminologyFoster careLawEpistemology

Abstract

fetched live from OpenAlex

Foster parents stand in for the community at large. For many children and youth, they may be the closest to responsible and caring adults they encounter. The work of foster mothers and fathers has unfolded in the context of social relations that make some groups and individuals more likely, acceptable, or able candidates. It has been highly gendered and ideally required expressions of model maternity and paternity. In the last half of the 20th century, such surrogates also increasingly emerged as part of therapeutic teams working toward the physical and psychological salvation of disadvantaged children and young people. Canada’s fostering adults have always negotiated an essentially border status. In modeling superior mothering or fathering or in operating alongside experts, they confronted a recurring dilemma of authenticity. On the one hand, most have received money and been subject to state supervision for duties judged preferably voluntary and private. On the other, most have lacked formal credentials in child study and protection agencies and governments have rarely paid professional wages. As surrogates and amateurs, they always struggled to be treated as more than inferior mimics of ‘real’ parents or ‘real’ experts. Women stand at the centre and men to the side of both conundrums.

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)0.042
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.060
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.030
Scholarly communication0.0080.012
Open science0.0020.011
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.236
Teacher spread0.210 · 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 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
Published2017
Admission routes1
Has abstractyes

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