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Record W3013819796 · doi:10.5206/fpq/2020.1.7316

Bearing the Brunt of Structural Inequality: Ontological Labor in the Academy

2020· article· en· W3013819796 on OpenAlexvenueno aff
Ruthanne Crapo, Ann J. Cahill, Melissa Jacquart

Bibliographic record

VenueFeminist Philosophy Quarterly · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityIdentity (music)Resistance (ecology)Labor relationsLabour lawWork (physics)ComplaintSociologyLaw and economicsLabour economicsPositive economicsEconomicsPolitical scienceLawEngineeringAestheticsPhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.258
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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