A method to analyze invisibility: Navigating the dissonance between woke and safe
Bibliographic record
Abstract
This article starts by considering how ‘the talk’ that black and non-black minority families give to their children comes as a duty to transfer the wisdom of how to be invisible forward through generations. It is not uncommon to think about being visible as a social good, but this is not quite so straightforward when one occupies a body deemed as ‘other.’ This article exposes this tension to explore how invisibility can be understood as an independent, complex, and nuanced social dynamic in its own right by considering literature that uses invisibility as an analytical lens, providing a synthesis of that literature to offer a preliminary multidimensional model of invisibility to extend extant tools for sociological study. This literature considers race, gender, sexuality, various presentations of power, and different social systems to demonstrate a model that identifies how the intersection of power, affect, presence, and voice fluidly transfigure across time and space to create an overall social construct of invisibility. This suggests that deeper development of a multidimensional construct of invisibility can provide a reasoned and valuable additional lens to address a range of social dynamics.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".