Locating the Traveller: Genni Gunn and Nostalgia on the Move
Bibliographic record
Abstract
This paper analyzes the concept of nostos through a reading of Italian-Canadian writer Genni Gunn’s autobiographical travelogue Tracks: Journeys in Time and Place (2013) to show how its narratives of movement contest meanings of home and homecoming. Gunn initiates new ways of thinking about return by taking the migrant traveler as its central figure and envisioning home as a “practice of displacement” (Evelein 21) wherein “home” is not achievable through physical return, but through memory. Specifically, Gunn subverts traditional notions of home by reimagining Italy through her travels to foreign places, which ultimately serve as sites of return to her homeland via cartographies of memory. In Gunn’s exploration of nostalgia, her narrative presents her identity as an Italian-Canadian immigrant as no longer defined by national borders, but rather as a condition of movement. Gunn uses the framework of travel to link acts of homemaking and homefinding so that the meaning of nostos emerges as a kind of dwelling-in-displacement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".