Bibliographic record
Abstract
In the posthuman era, what counts as ethical has dispersed and re-distributed to a nonhuman-centered network that includes nonhuman species and nonliving matter. This paper serves as a resistance and a possible way out against traditional human-pet ownership in therapeutic robot development. The paper consists of research and project-based speculative design that aims to recalibrate human relationships with others through the public interactive project Not-my-fish. In the project Not-my-fish, the others include fish, digital fish, and computers. The project is developed in a healthcare facility setting to offer an alternative healthcare modality with un-interactive interaction and flow as core concepts. The overarching research question is: what would a human-nonhuman relationship look like through the lens of Taoist-posthumanism, and how do we design such a relationship? More practically, how to create a mutually beneficial interactive system with limited human-initiated interaction in human-computer interaction? Firstly, I draw on Posthumanism, Taoism, and Contractualism as theoretical frameworks to form the foundation regarding the human-fish relationship. Second, I analyze and transcribe the theories into the project Not-my-fish in terms of un-interactive interaction. Third, I apply the concept of flow as design guidelines and feature four aspects of the flow in the human-fish interaction: ecological flow (the physical surroundings of the human-fish interaction), sensory flow (the physical senses and perception of humans and fish), social flow (the shared and unique social behavior of humans and fish), and data flow (the system’s memory load and data lifecycle). Fourth, I discuss how the participating parties, such as human individuals, computer-simulated fish, and institutional partners, could mutually benefit from this partnership. The project Not-my-fish rearticulates the relationship between humans and fish and humans and computers. It views the human-fish relationships from posthuman and Taoist perspectives to detach the idea of human-nonhuman ownership and promote reciprocity and equilibrium. Nevertheless, some challenges are presented for human partners and design aspects regarding the transition from human-centered to posthuman interactive media. However, this paper demonstrates a practical way for human society to develop a posthuman design in public interactives and serve the public good at the same time.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".