How epistemic frictions reconfigured the Quebecois contract archaeology normative structure
Bibliographic record
Abstract
Cultural resource management (CRM), or contract archaeology—archaeological work conducted upstream to land development projects—has come under criticism for enacting neoliberal and colonial agendas. While this criticism is sound, it is also “negative,” in that it entertains an alienating narrative by eschewing resistance attempts by CRM archaeologists. I draw on my own work as an organizer amongst Quebecois CRM workers, as well as on the process of epistemic frictions as the dissenting development of shared truths, to flesh out a “positive” criticism of the practice. Through three frictions that unfolded from 2017 to 2020, I show how CRM workers organized around the collective construction of their knowledge to define the constraints posed by their practice’s hierarchical class structure and reconfigure it through the definition of new norms. These frictions map what epistemic work will be needed to enact anticapitalist and decolonial change.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.045 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".