Bibliographic record
Abstract
Douglas Coupland is nominally a Canadian author, though he was born overseas on a NATO base in what was then West Germany. He has also subsequently spent considerable time in countries other than the one he is a citizen of, including the US, Japan and various European locations. In Coupland’s first novel Generation X, the narrator casually observes, speaking of the dating patterns and friendships of the three youthful protagonists, that nowadays everyone and everything seems to be either from nowhere or from Hell. The narrator invites the reader to muse on whether these two points of origin are really that different from one another… In my paper I examine the literary topography of Coupland’s story worlds in Generation X and more recent novels. The tension between presence and absence in this topography is palpable, and several Coupland plots involve characters trying to cope with living in a world consisting nearly entirely of non-places. In such a ‘Life After God’ (the title of a short story collection by Douglas Coupland) strategies for replenishment of meaning and belonging can be hard to come by – yet every Coupland story offers up hope for such strategies succeeding. Does this make Coupland a post-ironic, or post-cynical writer? And what role does place play in bolstering the hope of the characters?
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".