Whither Digital Archaeological Knowledge? The Challenge of Unstable Futures
Bibliographic record
Abstract
Digital technology increasingly pervades all settings of archaeological practice and virtually every stage of knowledge production. Through the digital we create, develop, manage and share our disciplinary crown jewels. However, technology adoption and digital mediation has not been uniform across all settings or stages. This diversity might be celebrated as reflecting greater openness and multivocality in the discipline, but equally it can be argued that such diversity is unsustainable, and that standards are insufficiently rigorous. Regardless, all positions face the possibility of being severely tested by some large-scale external event: on every continent we witness economic and political upheaval, violence and social conflict. How is digitally mediated knowledge created, managed, and disseminated by archaeologists today, and how secure are the means by which this is achieved? To investigate this question we apply the futurity technique of scenario analysis to generate plausible scenarios and assess their strategic strengths and weaknesses. Based on this analysis we propose some measures to place archaeology in a more robust knowledgescape without stifling digitally creative disruption. Publisher's Note A correction related to this article can be found here: http://doi.org/10.5334/jcaa.21
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".