Bibliographic record
Abstract
This essay investigates the knowledge produced around Ruth Ozeki’s novel A Tale for the Time Being through a discussion of its marketing processes and its reception, as well as through textual analysis. I first draw upon Sau-ling Wong’s observations about the problem of a US-centric referential framework in the internationalization of Asian American studies to examine a Western-centric framing in the marketing strategies of the US/Canada and the UK editions of Ozeki’s novel. Next, I turn to an examination of how reviews and selected readers’ responses to Ozeki’s novel show an at-times incoherent process of making sense of this text. In the latter part of the paper, I analyze the parallel depictions of Fukushima and Cortes Island, Ruth’s dreams, and Haruki #1’s diary in Ozeki’s novel. Attending to how Ozeki’s narratives destabilize the process of making sense, I argue that the novel is neither easy to read nor as transparent as the marketing strategies and reviews and readers’ responses suggest. The difficulties of making sense represented in A Tale for the Time Being thereby have the potential to intervene in a Western-centric, posivistic reading of the Asian other, challenging us to rethink the analytic frameworks we bring to bear while reading Asian American literary texts.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".