Fear of Black Consciousness By Lewis R.Gordon. Farrar, Straus and Giroux, 2022
Bibliographic record
Abstract
economic interests and humanitarian sentiments that appear to explain why different political leaders adopt different positions on questions of inclusion and exclusion.But Mavelli has a response to this.He explains that a neoliberal rationality of value can explain diverse responses to refugees by different states, each of which assigns a market value to not only economic but emotional concerns.He shows, for example, that the United Kingdom's decision to "invest" in just a few thousand Syrian refugees and Germany's decision to open its borders to many hundreds of thousands of refugees in the wake of Alan Kurdi's death in 2015 were both expressions of biopolitical care for the host population: a small number of Syrian refugees was a sufficient source of emotional capital for the people of Britain; a larger number of refugees served Germany's needs for emotional capital and also human capital in a time of labor shortages.As depressing as it may be, it is a highly compelling argument.Historically minded readers such as myself might wonder how and to what extent the logics of citizenship that Mavelli theorizes and documents are new.He does an excellent job of explaining these logics in the language of neoliberal rationality and he suggests that questions of inclusion and exclusion are "increasingly" subordinated to this rationality.But when and how did this emerge, and how does it play out in ways that are different from the logics that preceded it?Could an argument be made that the humanitarian inclusion of outsiders has, for a long time, been dependent on the "emotional value" that insiders have attributed to them, and, if so, is the difference today the fact that such practices have become intensified, refined, and/or subject to more conscious calculation, or perhaps something else?But that, of course, is not Mavelli's project.His project is to expound the neoliberal logics of citizenship that attribute market value, both material and emotional, to a diversity of people, from the uber-wealthy to the most poor and vulnerable, in ways that dictate their inclusion or their exclusion, their protection or their sacrifice.And in that, he has done a remarkable job.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".