Bibliographic record
Abstract
While I might trace this work back in spirit to a younger self who was fascinated with ghosts, secrets, underground chambers, trap doors, secret passages, and encrypted messages, I can also partially attribute my current theoretical interest in phantoms and haunted spaces to the fact that, when I was nearly ten, my father built a family room in our basement that included a bookcase that, when a concealed latch was released, swung open like a door.One could then walk through the opening which led through a passageway behind a wall.By following this passageway, one could emerge like a phantom through another concealed door on the opposite side of the room.These days, I'm told this house is haunted.This book traces the trajectories of such hauntings.Over the past few years, I have been fortunate to have had the time, the encouragement, and the financial support to explore what was originally a childhood fascination with all things Gothic and which became a critical analysis of the social and cultural dimensions of haunting where these can be seen to resonate in literary and cultural studies, in philosophy, and psychoanalysis.Research and preparation of this work was originally assisted by a Graduate Fellowship from The University of British Columbia which went towards early speculations, and by a Doctoral Fellowship from the Social Sciences and Humanities Research Council of Canada which provided the opportunity to make manifest those intimations.More recently,
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.322 | 0.226 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".