Heck Fun Ya! (Let's Eat!) An Autoethnographic Cookbook: Memories of a Chinese-Canadian Upbringing Beyond
Bibliographic record
Abstract
This dissertation emerges out of the conceit that food, more than mere sustenance, is significant cultural artifact. Rooted in the premise that there is educative value in life story as research, Heck Fun Ya! (Let’s Eat!) An Autoethnographic Cookbook: Memories of a Chinese-Canadian Upbringing Beyond is a performative text using food as a vehicle to discuss culture, identity, and otherness. Blending academic analyses, life story writing, and multi-media, its creative format is an expression of the uniqueness of this process. Thematically organized, the cookbook structure of starters, mains, and desserts is comprised of food experiences punctuated by recipe in a variety of narrative forms. Narratives will be analyzed through interdisciplinary lenses that explore how food shapes various identities in order to ask: What role does food play in identity construction? And how can storying and/or re-storying food experiences be used to live a good life? In approaching this, the work aims to recalibrate my understanding of past experiences to create resonance with new ones, and thus, deepen knowledge. Starters offers two opposing effects of food experience: one that considers food shame and stigma through a psychoanalytic lens (Kristeva, 1982; Goffman, 1963), and the other that explores the role of disgust in igniting imagination (Korsmeyer, 2011). Mains will address the part food plays in the identities that emerge from having a sense of home and belonging. Narratives serve as entry points for discussions that emphasize the home, food, eating, and family connection (Bell Valentine, 1997; Blunt Dowling, 2006). Food’s role in identities that evolve out of the things we do are also considered: teaching and food writing including the physiology of taste, impact of hunger and whether it sharpens one’s descriptive abilities (Brillat-Savarin, 1949/2009; Hemingway, 1964/2009; Zola, 1873/2009). Desserts, the last cookbook section, takes a dramatic turn to consider the non-alimentary ways of food and feeding; this discussion explores music, yoga, meditation, and narrative medicine as expressions of “nourishment” (Pretorius, 2017; McGonigal, 2008; Lazar et al., 2005; Charon, 2008). Finally, the significance of the study and implications for next steps will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".