Locating Creative Agency in Archaeological Data Work
Bibliographic record
Abstract
The workflows that are now commonplace across archaeological projects mask social and epistemic structures and principles. More specifically, they re-distribute creative agency to promote specific kinds of outcomes based on discrete data models. This paper draws attention to the mechanisms through which data are created and curated, focusing on the social and technical apparatus through which archaeologists control the creation and flow of information. Based on observations of and elicitations about archaeological data work in fieldwork settings at two cases, I articulate how the management of data and of labour are inherently intertwined, and how workflows are operationalized by managerial systems to ensure that data are created and curated toward productive ends. This paper therefore contributes to ongoing theory-building and prompts further reflection on the roles of information objects, infrastructures and professional relationships that mediate the valuation, validation and legitimization of archaeological knowledge.
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 | 0.042 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.017 | 0.107 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".