Bibliographic record
Abstract
In 1973, an American archaeologist named Dr. William Rathje sought to create a method that would help his students understand the intricacies of archaeological fieldwork. Dr. Rathje recognized that his students at the University of Arizona were having a difficult time understanding cultural remains from the past (Rathje, 1979, p. 4), so his idea was to use contemporary cultural material waste as a study tool. He named this method “The Garbage Project.” Given that the project took place during 1970s and students of the time were far removed from potsherds and post holes, it made sense to articulate archaeological sites in a contemporary way. Over time, this process would come to be known as garbology, which has come to inform both past research and present-day disciplines such as economics and public policy. This paper will outline the cross-discipline benefits that archaeology brings to modern society, including how it informs us about sustainability issues and how human societies interact and identify with their waste.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.027 | 0.040 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".