Auto-Ethnography: Connecting to the Nearby
Bibliographic record
Abstract
This project delves into the establishment of place attachment in evolving landscapes through an interdisciplinary lens. It starts with the interpretation of the story of A-Fei, a mushroom forager in Yunnan, China from the perspective of multispecies ethnography, revealing that place attachment is tied to the nearby, where everyday interactions with the surrounding landscape can evoke memories of hometown and generate meanings of a new residence. Extending these insights, this project adopts auto-ethnography to examine the author’s experiences in the multicultural city of Toronto to explore how she as an immigrant builds an attachment to the local landscape. Through sensory engagement, cultural observation, and interviews of the other immigrants, how magnolias facilitate a new sense of belongings has been found. This project aims to transcend disciplinary boundaries and expand the realm of landscape architecture to anthropologic perspectives. By emphasizing the co-evolution of human and non-human lifeways, it seeks to explore how individuals perceive landscape and build relationship with it and proposes “ethnographizing landscape architecture” as a value-centered approach for socially impactful and contextually relevant design.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".