Erasing the past and returning to it: digital technology and writerly identities in Amnesia by Peter Carey and The Wisdom Tree by Nick Earls
Bibliographic record
Abstract
This paper examines the ambivalent representations of digital technologies in Amnesia (2014) by Peter Carey and The Wisdom Tree (2016) by Nick Earls with particular regard to the ways in which these technologies are presented as challenging or complicating the role and identity of the writer as understood in print culture. The protagonist of Carey's Amnesia, Felix Moore, is presented as a formerly influential print-based journalist who is now sliding into irrelevance in a digital age. In Earls' The Wisdom Tree, the only novella in the five book sequence that is set in the past is Vancouver, which depicts its protagonists pursuing literary careers in 2001, in the uneasy wake of 9/11 and on the cusp of the digital revolution. References in the novellas that precede and follow Vancouver reveal that these writers are no longer conventionally successful or relevant in a contemporary setting. Where the approaches of Carey and Earls diverge is around the theme of memory. In Amnesia, Moore is commissioned to ghost write the autobiography of an enigmatic hacker known as 'Angel', and in so doing links her seemingly inexplicable and globally focused acts of cyber-terrorism to a very specific thread of Australian history. In doing so he reasserts his relevance as a writer by challenging the 'amnesia' of the contemporary digital age. The novellas of the Wisdom Tree, by contrast, presents a more complex exploration of digital technology , noting that the sudden associative connections it encourages have the potential to both obscure the past and return people to it, including (if only momentarily) the seemingly lost literary lives and preoccupations explored in Vancouver.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".