Bibliographic record
Abstract
We are nowhere near ‘peak waste.’ Hoornweg et al, 2014: 117 When we read news about waste – plastic bags clogging water drains in India and causing contaminated drinking water, child labour used to dismantle used electronics in Malaysia, space junk orbiting earth or PPE masks washing up on our shores – our waste problem seems clear: waste is being mismanaged! The solution appears similarly obvious: we must better manage our waste! But the devil, as the famous idiom goes, is in the details. What we understand the problem to be – the mismanagement of waste – depends on how we actually define waste. Whose waste are we referring to? Who should be responsible for managing it better? And what, more specifically, would register as ‘better’ waste management? At first glance, the definition of waste seems equally obvious. Waste is all of that stuff that we once wanted but no longer want (Strasser, 1999). Waste is all of those things we put in our trash can, and if our local waste services are functioning well, are whisked away from our homes to quickly become out-of-sight and out-of-mind. Yet, waste turns out to be a rather complex problem involving different rightsholders and stakeholders, temporalities, geographies, political economies, transnational agreements, regulations and policies, and cultural traditions that disproportionately affect a range of publics. To introduce this complexity, let’s consider the following waste snapshots: Snapshot 1 The Republic of the Marshall Islands is a United States associated state and comprises some 1,156 islands in the Pacific Ocean, north of New Zealand. With a total population of just over 58,000, most of its territory (over 97 per cent) is water. Beginning around the 10th century, successive waves of colonizers and settler colonizers claimed the islands, from Micronesians, to Spanish, to German, to Japanese, and finally to Americans during World War II. The US began nuclear bomb testing on the Marshall Islands’ Bikini Atoll in 1946 and continued detonating nuclear arms for over a decade. During this period, the US exploded 23 nuclear weapons, first above-ground and then underground. The second – Baker test – detonation contaminated all of the surrounding ships, leading Glenn T. Seaborg, chair of the Atomic Energy Commission, to call it “the world’s first nuclear disaster” (in Weisgall, 1994: ix).
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 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.335 | 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 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".