Reflections on a Counter-Humanist Archaeology: A Commentary on Greer 2023
Bibliographic record
Abstract
In ‘Humanist Missteps’, Matthew Greer makes the pointed observation that non-anthropocentric frameworks, including symmetrical, object-oriented and posthuman feminist archaeologies, have primarily focused on deconstructing the human–non-human binary while failing to problematize humanist assumptions about who counts as Human. At the core of Greer's argument is the matter of citational practice: which social theorists are archaeologists referencing in their efforts to craft relational approaches to humans, things, animals and plants? In answering this question, the author points to a notable lack of Black Studies theorists, particularly the work of Sylvia Wynter, Zakkiyah Jackson and Tiffany King, in posthumanist archaeologies. While I agree with Greer's critiques, his essay stops short of explaining this citational silence. In this brief commentary, I suggest that this absence of Black Studies scholarship reflects the fact that the discipline of archaeology remains a ‘white public space’ (Brodkin et al.2011: 545) and maintains an artificial division between analysis and activism.
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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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.049 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.056 | 0.052 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".