February is the Cruelest Month: Neoliberalism and the Economy of Mourning in Lisa Moore’s February
Bibliographic record
Abstract
IN TAKING STOCK of the present state of literature in Newfoundland and Labrador, a crucial development over the last twenty years is evident — the emergence of a palpably cosmopolitan and globalized sensibility. This is particularly the case in the work of urban writers such as Michael Winter, Jessica Grant, Edward Riche, and Lisa Moore. Not only are the characters in their fiction cosmopolitan globe-trotters who are plugged into an international popular culture, but their work also reflects a preoccupation with the sophisticated technology, mobility, flows of trade, and geopolitical relations that characterize our present globalized milieu. At the same time, there is ambivalence about globalization in their work. That is especially important to underscore because celebratory interpretations of globalization (characterized as a rise in global prosperity brought about by greater mobility and financial and technological innovations) have been increasingly critiqued for effacing — even providing covering fire for — an underlying institutionalization of a neoliberal ideology. As theorists such as Zygmunt Bauman, Pierre Bourdieu and David Harvey have highlighted, neoliberal thinking — which privileges deregulation, privatization, the easing of financial transactions, diminution of governmental involvement in the economy, and reduction of the public sector — has facilitated and justified a vast, global redistribution of wealth. For Harvey, the triumph of neoliberalism’s positing of itself as a kind of world-wide common sense is evident in the treatment of the unprecedented concentration of wealth in “the world’s major financial centres” as “a mere and in some instances even unfortunate byproduct of neoliberalization. The very idea that this might be ... the fundamental core of what neoliberalization has been about all along appears unthinkable.” (119)
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".