Remaking KM6: an authoethnographic and photographic journey to Santa Cruz Bolivia
Bibliographic record
Abstract
This paper examines the process of autoethnography and photographic study within the context of hybridity and diasporic cultural identity. For this project, ReMaking KM6, I returned to my childhood home of Santa Cruz, Bolivia creating representations of this city in the context of Latin America. Through the lens of hybridity, I look at my own process of autoethnography and documentary photography. The concept of hybridity resonates in my personal narrative, which illustrates the meeting between Korean, Bolivian and Canadian cultures. As a result, the visual evidence gathered demonstrates a specific perspective and contributes to the pool of Latin American images available in North America. The photographs gathered during my fieldwork illustrate the day-to-day lives of Crucenos (people of Santa Cruz) that disrupt notions of Latin America as static, and exotic providing an alternative to stereotypes of this region. In order to illustrate specific examples of this process, this essay will refer to the seventy-page book titled ReMaking KM6: Childhood Memories of Santa Cruz, Bolivia, that outlines this journey through the combination of visual evidence and narratives. My autoethnography takes on the process of "memory-work" in which I explore the relationship between my childhood memories and present-day reality in Santa Cruz, Bolivia. By looking at Latin America as a concept, I create a specific local and regional representation of Latin America that shows one of the many diverse narratives within this hybrid cultural ground. Through careful examination of my role as former-resident, present-day foreigner, North American, artist, researcher, academic and photographer, I negotiate the meanings in my photographs by contextualizing my hybridity and subjectivity. Some of the main questions that I tackle in this essay are: what kinds of tools can be used in order to engage in an auto ethnography that exists within the representation of place? What are the characteristics of diasporas, and hybrid identities that renounce positivist notions of static and dichotomous identities and embrace identity as a constant flux? What constitutes Latin American photography? What have been the tendencies in Latin American photography? How do artists and photographers that live or have strong connections to Latin America portray this vast area? Can photography be an effective tool in representing diversity?
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".