Figurations of Digital Practice, Craft, and Agency in Two Mediterranean Fieldwork Projects
Bibliographic record
Abstract
Abstract Archaeological practice is increasingly enacted within pervasive and invisible digital infrastructures, tools, and services that affect how participants engage in learning and fieldwork, and how evidence, knowledge, and expertise are produced. This article discusses the collective imaginings regarding the present and future of digital archaeological practice held by researchers working in two archaeological projects in the Eastern Mediterranean, who have normalized the use of digital tools and the adoption of digital processes in their studies. It is a part of E-CURATORS, a research project investigating how archaeologists in multiple contexts and settings incorporate pervasive digital technologies in their studies. Based on an analysis of qualitative interviews, we interpret the arguments advanced by study participants on aspects of digital work, learning, and expertise. We find that, in their sayings, participants not only characterize digital tools and workflows as having positive instrumental value, but also recognize that they may severely constrain the autonomy and agency of researchers as knowledge workers through the hyper-granularization of data, the erosion of expertise, and the mechanization of work. Participants advance a notion of digital archaeology based on do-it-yourself (DIY) practice and craft to reclaim agency from the algorithmic power of digital technology and to establish fluid, positional distribution of roles and agency, and mutual validation of expertise. Operating within discourses of labour vs efficiency, and technocracy vs agency, sayings, elicited within the archaeological situated practice in the wild, become doings, echoing archaeology’s anxiety in the face of pervasive digital technology.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".