Digital Refuse: Canadian Garbage, Commercial Content Moderation and the Global Circulation of Social Media’s Waste
Bibliographic record
Abstract
The story of a rogue Canadian garbage barge attempting to offload illegal garbage in the Philippines opens this article on techno-trash, in order to underline both the relationships between countries of the Global North with countries of the Global South in matters of waste, as well as to reframe discussions of techno-trash as fundamentally tied to material things. The definition of techno-trash is then expanded, to cover digital detritus created through an entirely digital set of practices I term “Commercial Content Moderation.” The attempt to offload mounds of e-waste and the similar ways in which a great deal of physical trash circulates around the globe are then directly connected to the kind of disposal that CCM workers do, increasingly undertaken in sites like the Philippines, the Business Process Outsourcing (or BPO) capital of world. Such e-waste arrives in the archipelago for dismantling, repurpose and storage, alongside outsourced CCM, work with many of the objects now deemed “waste” once crucial to the production of the very material for which CCM workers now screen and remove.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".