Bibliographic record
Abstract
The following chapter by Lai-Tze Fan is a critical and creative reflection that describes the research-creation project e-Waste Peep Show; or, on Seeing and not Wanting to be Seen (EWPS). Since research-creation as an academic practice challenges scholars to merge creative approaches in various disciplines and to apply theory to practice, it allows scholarship to address twenty-first-century issues in innovative ways. Constructed as an art installation, EWPS features original footage of an e-waste (electronic waste) plant in Northern Hong Kong through a peephole in its walls. The camera captures a masked woman taking apart mounds of technological trash. Suddenly, she throws technological debris at me: don't look at me; don't film here. In this chapter, the author describes the process of constructing the installation such that spectators can experience the act of peeping onto sites and sights that they are not ‘supposed’ to see. The three parts of the paper describe the fragments that came together to produce the research-creation project: first, the author discusses the toxicity of e-waste and the exploitation of e-waste labourers, with a focus on East, Southeast and South Asia; second, she describes the fieldwork that I completed in December 2017 to collect video footage at an e-waste plant in Hong Kong; third, she details the creation process of the installation and the intended experience for the spectator-as-user. In doing so, this chapter aligns creative methods in sustainable research with an ethical intervention into global technological consumerism.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.027 |
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".