Beyond Considering Surrogates’ Reports at ‘Face Value’: Theorizing and Contextualizing the Autonomy-Related Threats of Surrogacy Arrangements
Bibliographic record
Abstract
As research on surrogacy in Canada is only emerging, this thesis seeks to incite discussion relating to the autonomy of surrogates by analyzing recent studies which capture the experiences of surrogates through surveys and interviews. Much of the current literature on surrogacy focuses on issues around commercialization which are less applicable to Canada where surrogacy is altruistic. Moreover, many scholars have either discussed the autonomy of surrogates only from a theoretical perspective, neglecting surrogates’ personal accounts, or have assumed that the reports of surrogates should be considered at ‘face value.’ Ultimately, I show how the reports of surrogates should be acknowledged but it is also important to consider contextual factors, such as whether the reports may be influenced and shaped by the constraints of surrogacy arrangements. While the reports of surrogates reveal the ways surrogates experience and often manage and resist autonomy-related threats, and in turn call into question theoretical concerns about surrogates lacking autonomy and power, certain theoretical concerns remain which are not identified in the studies, either because of empirical limitations or a failure to engage with them. Overall, my discussion is action guiding: I aim to shape emerging scholarship on surrogacy, so that it accounts for the complexity and nuances of surrogates’ experiences, and I gesture to certain policy interventions which follow from my discussion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.084 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".