Ethics as Capital: Eggs, Research Governance and the Politics of Representation
Bibliographic record
Abstract
In 1999 I interviewed a 29 year old woman, Nora, in a small northern Canadian city about her thoughts, plans and expectations about getting pregnant. Although she had tried to get pregnant before and was still hoping to, she had gone back to university and had decided to put the plans to become pregnant on hold. During the conversation, I mentioned an article that had recently appeared in the New Yorker magazine (Mead 1999) reporting that women were being paid U.S. $15,000-$20,000 to “donate” their eggs for fertility purposes. Nora looked at me, questioningly: “$15,000-$20,000?” “That was a high amount,” I replied. She nodded, “Hmm. It sounds too good to be true.“ I described the content of the article a bit more, as well as the advertisement that had inspired it. Nora reflected: But, I mean, the donor ... I mean it’s, if she wants to, it’s her responsibility to be responsible to know what’s happening to her body. But if somebody is looking for a donor, there’s nothing wrong with that. But is education provided out there? Or are resources out there for women to research them? Because, if somebody put in an ad and I saw the ad, and, you know, $20,000 ... Yeah, I’d want to do it. But I’d also want to check, have the resources to look into it. What’s going to happen to my body taking fertility [drugs]? How do I match my cycles to the other person’s cycle? And how much damage are they going to do to my body when they do surgery? And do I have to go for the surgery, or ...?
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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 | 0.138 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.110 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".