Archaeological data work as continuous and collaborative practice
Bibliographic record
Abstract
This dissertation critically examines the sociotechnical structures that archaeologists rely on to coordinate their research and manage their data. I frame data as discursive media that communicate archaeological encounters, which enable archaeologists to form productive collaboration relationships. All archaeological activities involve data work, as archaeologists simultaneously account for the decisions and circumstances that framed the information they rely on to perform their own practices, while anticipating how their information outputs will be used by others in the future. All archaeological activities are therefore loci of practical epistemic convergence, where meanings are negotiated in relation to communally-held objectives. Through observations of and interviews with archaeologists at work, and analysis of the documents they produce, I articulate how data sharing relates distributed work experiences as part of a continuum of practice. I highlight the assumptions and value regimes that underlie the social and technical structures that support productive archaeological work, and draw attention to the inseparable relationship between the management of labour and data. I also relate this discursive view of data sharing to the open data movement, and suggest that it is necessary to develop new collaborative commitments pertaining to data publication and reuse that are more in line with disciplinary norms, expectations, and value regimes.
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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 | 0.043 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.080 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.005 | 0.027 |
| 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".