Through the looking-glass: Objectivity, interpretation, and the construction of social kinds as emerging issues in research ethicss
Bibliographic record
Abstract
Background: Access to the means of knowledge production is severely limited for many individuals who are socially, politically, and economically disadvantaged. Of particular concern, largely overlooked in bioethics discourse, is the manner by which surveillance, identification, and classification contribute to the creation of particular populations based on harmful social identities, and the subsequent stigmatization of the individuals subject to analysis. Methods: Drawing from a range of philosophical and sociological literature, including Hans-Georg Gadamer, Charles Taylor, Bruno Latour, and Deborah Lupton, as well as public health discourse, this paper gives an analysis of a challenging metaphysical and ethical problem related to research practices and public health interventions in vulnerable communities. The themes of objectivism and stigmatization are illuminated by focusing on research related to HIV infection among survival sex workers in Vancouver’s downtown eastside. Results: Reductionist models of human behaviour presuppose a clear distinction between description and evaluation, contributing to an understanding of social reality as structured by objective, base level data, and misconstruing modes of social relations as individual action. In the context of HIV risk, an ethical problem emerges: describing individual actions as “data,” or “fact,” serves to construct and reinforce transgressive social identities. Given that stigmatization is itself part of the context of adverse health outcomes and HIV infection, practices relying on methods that presume representational authority and fail to critically interpret human action are ethically problematic. Resolving difficulties within the methodological underpinnings of public health and HIV behavioural research requires an understanding of many social science methods as hermeneutical, while enabling the contribution of disparate standpoints to the production of scientific knowledge. Conclusions: The implications of presumed objectivity and representational authority demand a reconsideration of how research in vulnerable communities might be conducted, such that it produces results that are scientifically rigorous and socially responsible.
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 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 | 0.097 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.017 | 0.291 |
| Scholarly communication | 0.030 | 0.042 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.011 |
| 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".