When Facebook plays matchmaker: Interactions within an online community dedicated to surrogacy and egg donation
Bibliographic record
Abstract
Abstract Objective This article explores the interactions and digital practices of people involved in an online community dedicated to surrogacy and egg donation in the province of Québec, Canada. Background Sociodigital networks, with the emergence of platforms such as Facebook groups, provide a space to discuss assisted reproduction, seek advice, offer support, and connect with other Internet users to negotiate and establish a third‐party reproduction agreement. Method This study is based on a long‐term ethnographic field within a Facebook group, and individual interviews conducted with 22 members of this community. The data collected were analyzed inductively according to the principles of grounded theory. Results Three themes emerged from the online interactions and stories of the women we met. First, the publication of intended parents' testimonies is the preferred method of finding a surrogate or egg donor match and is reminiscent of the language and rituals associated with dating sites. Second, the expression of legal and financial concerns occupies a prominent place in the group discussions, given the lack of a legislative framework in this Canadian province and the variability of interpretations of the current legal framework. Third, the choice made by surrogates and donors to be involved in an online community is not random and sometimes indicates of a desire to establish a sense of control over the process and to negotiate the modalities without the presence of an intermediary. Conclusion and implications Facebook groups dedicated to infertility and assisted reproduction appear to be more than simply matchmakers, as the interactions that occur there perform various functions. In the absence of a formal organization dedicated to parents, surrogates, and donors in Québec, the online community becomes a place for information sharing, support, and networking. This offers avenues of intervention for professionals who need to reflect on and consider how online communities dedicated to third‐party reproduction may influence their practice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".