Longing in the Past, Belonging in the Future: An Autoethnographic Fiction
Bibliographic record
Abstract
In this autoethnographic writing, we explore the concepts of longing and belonging through a collaborative writing process that is fictional at times and autoethnographic at times. We present an experimental and arts-based approach to analyzing and understanding memories, and themes of nostalgia, belongingness, and longing in the present day. Through our autoethnographic fiction (Bochner and Ellis 2016; Ellis 2004) we explore questions such as: what is it like to long and belong, what is it like to long for a future that is embedded in the past, what is it like to futurize/co-futurize memories, and what if the past is the pre-present? As immigrants to Toronto, coming from nations that were once colonized, and still remain in the peripheries of colonization, we ponder about our bodies occupying the third space that we are living in, the feelings of nostalgia and belonging in our fiction. We write about our belongingness to our roots and the trajectories of our beings and think what decolonizing the the concept of memories might evoke. Methodologically, we draw from Erin Manning’s (2016) idea of going against method to propose a collaborative autoethnographic fiction writing and collaging practice that implicates our memories and bodies with our surroundings and other bodies, human, beyond human, and material, as instruments of research. We suggest that the decolonization and dehistoricization of memories and our conceptions of longing, belonging, and creating futures embedded in the past can happen by futurizing our notions of memories. We hope that writing a fiction in conversation with one another and in synchronicity of each other’s experiences will allow us to deconstruct and problematize our understanding of memories, the frictions between avant-garde and nostalgia and interspersing the collaging practice will allow us to build our stories and explore belongingness and nostalgia, longing for something indefinite and unwanted memories.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".