Data, Annotation, and Meaning-Making: The Politics of Categorization in Annotating a Dataset of Faith-based Communal Violence
Bibliographic record
Abstract
Data annotation is a process of meaning-making and is inherently political. The literature on ethics in data-driven technologies explores these political aspects, primarily focusing on questions of bias and power. This paper argues that the politics of annotation often overemphasize secular and modern values and overlooks faith-based, religious, and spiritual aspects (FRS) in data annotation. This oversight particularly affects the postcolonial regions of the Global South, where FRS are intertwined with people’s everyday experiences and ethics. We conducted a focus group discussion and contextual inquiries with six annotators who annotated a faith-related “violence” dataset from South Asian YouTube content. Our analysis reveals that FRS blindness in data annotation manifests through the politics of achieving objectivity and the “scientific” process of meaning-making. Due to these goals, which are predominantly shaped by Western values, FRS sensitivities are overlooked from the initial stages of data curation through annotation, ultimately leading to a context collapse within the annotation process. Finally, we advocate for the adaptation of FRS sensitivities into the annotation process and data infrastructure, particularly when the dataset clearly pertains to FRS, to promote greater cultural and contextual inclusivity in annotation practices.
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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 | 0.164 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".