Bibliographic record
Abstract
The publication of a revised and enlarged edition of Cristina Artenie's Gothic and Racism 1 (2023) is timely.Over the last decade or so, gothic has emerged as a key mode in which contemporary cultural production is interrogating racial violence.A small sample of work demonstrating this development might include, for example, Helen Oyeyemi's White is for Witching (2009), Jordan Peele's Get Out (2017), Jesmyn Ward's Sing, Unburied, Sing (2017) or Remi Weeke's His House (2020).Corresponding to the appearance of these and related fictions, new directions have emerged in Gothic Studies, with scholars foregrounding the resistant politics of minoritized gothic traditions and exploring justice-centred frameworks such as decolonial thinking.2 Appearing in this contemporary context, Gothic and Racism also joins with critical trajectories established since the late-20th century.These consider how gothic images have long been leveraged in the production of popular racial imaginaries; how gothic provided a racialised language that both provoked and allayed imperial anxieties; and how, in the hands of postcolonial writers, gothic can function as a discourse for critiquing colonial regimes or for giving haunting voice to histories of trauma and cultural erasure.3 Taken together, all these various critical engagements provide a sense of gothic's long and intimate involvement with the materially efficacious fiction of race, and with the systems and practices of racism that, on a global scale, have organised socio-ecological realities in its violent image.4 It is surprising, then, that few full-length and wide-ranging studies of gothic and racism exist, and still more so given that gothic first appears in an 'Enlightened' and industrialising Britain culturally and materially powered by histories of enslavement and colonisation.5 A collection broadly titled Gothic and Racism would be well-placed to address how, from its inception, gothic registers, elides, colludes with or critiques a world produced by racialised systems of power, and, encouragingly in this respect, Artenie's volume is fuller in historical and regional scope than most existing analogous studies.6 The book covers the period from the 19th to the 21st centuries and includes chapters on Britain, Russia, Canada, the Caribbean, India and the United States.It also adopts a productively generous definition of
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".