Remember you will [not] die: Mortality versus immortality in a world of patterns and randomness
Bibliographic record
Abstract
This project seeks to explore our perception of death, mortality, mourning processes and death-related rituals within the online world. I specifically look at the Social Network site Facebook as a case study. I argue that new media - such as digital technology like the Internet - is transforming the way we see ourselves and the world, affecting our perception of death and death-related aspects, yet not replacing offline practices. My exploration takes Katherine Hayles’ notion of pattern/randomness as the key theoretical axis and lens of my research. \n \nI strengthen my argument by creating a fictional future scenario situated ten years from now in a Western developed cultural context (i.e. Toronto), where trends of online practices are exaggerated. My studio work, in this scenario, are mourning pieces. It takes the form of wearable technology products that convey Facebook data of a deceased user, and are meant to be used by the bereaved. However, I have created these products from a critical design perspective, wherein products work as commentary, provocation to the public and a way to open up discussion on the topic. \n \nThis thesis project is interdisciplinary in content and form, looking at and exploring fields such as critical theory, philosophy, social science, critical design, various design practices and advertising.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".