EmboDIYing Disruption: Queer, Feminist and Inclusive Digital Archaeologies
Bibliographic record
Abstract
Inclusive approaches to archaeology (including queer, feminist, black, indigenous, etc. perspectives) have increasingly intersected with coding, maker, and hacker cultures to develop a uniquely ‘Do-It-Yourself’ style of disruption and activism. Digital technology provides opportunities to challenge conventional representations of people past and present in creative ways, but at what cost? As a critical appraisal of transhumanism and the era of digital scholarship, this article outlines compelling applications in inclusive digital practice but also the pervasive structures of privilege, inequity, inaccessibility, and abuse that are facilitated by open, web-based heritage projects. In particular, it evaluates possible means of creating a balance between individual-focused translational storytelling and public profiles, and the personal and professional risks that accompany these approaches, with efforts to foster, support, and protect traditionally marginalized archaeologists and communities.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.114 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".