“You Are Turning into a Hive Mind”: Storytelling, Ecological Thought, and the Problem of Form in Generation A
Bibliographic record
Abstract
This article discusses the relationship between literary form and contemporary ecological anxiety in Douglas Coupland’s novel Generation A. Coupland’s speculative fiction envisions a possible future in the wake of Colony Collapse Disorder, but the more generalized eco-anxiety the novel explores is applicable to a number of contemporary environmental issues ranging from climate change to ocean acidification. I argue that Coupland’s novel invites readers to consider the problem of representing ecological problems characterized by global scale, temporal uncertainty, and multiple origins. I then explore how Coupland responds to these challenges by stretching form in two directions. First, he juxtaposes and recycles a series of stories in a manner that capitalizes on lateral, shortened forms of attention, leading readers to detect larger patterns of significance within a database of what might initially seem like insignificant or banal details. Second, he cultivates the development of a form of “hive mind” among characters and readers that stretches ideas of personhood beyond the corporeal boundaries of the individual subject. The latter opens new possibilities for conceiving of a collective, networked mode of political agency in the era of social media and global scale effects.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".