Confronting Archaeology’s “Gray Zones”
Bibliographic record
Abstract
Abstract Drinking culture. What happens in the field. It was just a joke. Don’t rock the boat. Archaeology staggers under the weight of its many “gray zones,” contexts of disciplinary culture where boundaries, relationships, ethical responsibilities, and expectations of behavior are rendered “blurry.” Gray zones rely on an ethos of silence and tacit cooperation rooted in structures of white supremacy, colonialism, heteropatriarchy, and ableism. In the gray zone, subtle and overt forms of abuse become coded as normal, inevitable, impossible, or the unfortunate cost of entry to the discipline. Drawing on narrative survey responses and interviews collected by the Working Group on Equity and Diversity in Canadian Archaeology in 2019 and 2020, we examine the concept of the gray zone in three intersecting contexts: the field, archaeology’s drinking culture, and relationships. The work of making archaeology more equitable relies on our ability to confront gray zones directly and collectively. We offer several practical recommendations while recognizing that bureaucratic solutions alone will not be sufficient. Change will require a shift in archaeological culture—a collective project that pulls gray zones into the open and prioritizes principles of care.
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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.108 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".