Walking Towards the Past: Loss and Place in Jane Urquhart’s AMap of Glass
Bibliographic record
Abstract
In this paper I examine the notions of lossand placein relation to the mourning of our personal and historical pasts.2 In doing so, I draw upon the psychoanalytical writings of Julia Kristeva in an analysis of Jane Urquhart’s 2005 novel AMap of Glass. Iread AMap of Glassin the winter of 2006, just after having submitted the final draft of my dissertation to my examination committee for oral defense. My doctoral dissertation investigated Kristevan psychoanalytical and literary theory and how the processes of separation, loss and idealization are connected to “lost nature”—and how, in Kristevan thought, nature is ahistorical past. Thus, as I read the novel I was particularly interested in how the themes of loss and mourning areexplored in Urquhart’stext.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".