An examination of voice in contemporary Canadian fiction \nand Ballistics : a novel \n
Bibliographic record
Abstract
When the term “voice” is used in the discussion of contemporary fiction – as it frequently is – its meaning \nis taken to be understood intuitively, and more often than not no further elaboration is either required or \noffered. In these essays – augmented by my novel, Ballistics, which has more than once been described as \nhaving a “very distinct voice” – I examine what, exactly, we mean (or think we mean) by the term “voice” \nwhen we use it in our discourse about fiction: it turns out that what we know intuitively does not so \nelegantly hold up under scrutiny. I examine the standardized methods for both talking about “voice” and \nimproving it in one’s own fiction, as put forward by luminary novelists and teachers like John Gardner and \nJack Hodgins, and I suggest that part of the reason why discussion of “voice” is limited to what we feel, \nintuitively, is because voice is not embedded in the text, but, instead, constitutes the experience – the \nwhat it’s like – of engaging with a work of literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".