Bearing the Brunt of Structural Inequality: Ontological Labor in the Academy
Bibliographic record
Abstract
Empirical data show that members of underrepresented and historically marginalized groups in academia undertake many forms of undervalued or unnoticed labor. While the data help to identify that this labor exists, they do not provide a thick description of what the experience is like, nor do they offer a framework for understanding the different kinds of invisible labor that are being undertaken. We identify and analyze a distinct, undervalued, and invisible labor that the data have left unnamed and unmeasured: ontological labor, the work required to manage one’s identity and body if either or both do not fit into academic structures, norms, and demands. We argue that ontological labor efforts should be understood as a form of labor. We then provide a characterization of ontological labor, detailing the labor as navigating one’s obligations to give and managing entitlements to take. We also highlight the ontological labor that takes place through instances of resistance, such as through complaint or refusals.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.069 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".