THE ARCHAEOLOGY OF THE POSTINDUSTRIAL: SPATIAL DATA INFRASTRUCTURES FOR STUDYING THE PAST IN THE PRESENT
Bibliographic record
Abstract
Postindustrial urban landscapes are large-scale, complex manifestations of the past in the present in the form of industrial ruins and archaeological sites, decaying infrastructure, and adaptive reuse; ongoing processes of postindustrial redevelopment often conspire to conceal the toxic consequences of long-term industrial activity. Understanding these phenomena is an essential step in building a sustainable future; despite this, the study of the postindustrial is still new, and requires interdisciplinary connections that remain either unexplored or underexplored. Archaeologists have begun to turn their attention to the modern industrial era and beyond. This focus carries the potential to deliver new understandings of the industrial and postindustrial city, yet archaeological attention to the postindustrial remains in its infancy. Developments in the ongoing digital revolution in archaeology and within the social sciences and humanities have the potential to contribute to the archaeological study of the postindustrial city. The development of historical GIS and historical spatial data infrastructures (HSDIs) using historical big data have enabled scholars to study the past over large spatial and temporal scales and support qualitative research, while retaining a high level of detail. This dissertation demonstrates how spatial technologies using big data approaches, especially the HSDI, enhance the archaeological study of postindustrial urban landscapes and ultimately contribute to meeting the “grand challenge” of integrating digital approaches into archaeology by coupling reflexive recording of archaeological knowledge production with globally accessible spatial digital data infrastructures. HSDIs show great potential for providing archaeologists working in postindustrial places with a means to curate and manipulate historical data on an industrial or urban scale, and to iteratively contextualize this longitudinal dataset with material culture and other forms of archaeological knowledge. I argue for the use of HSDIs as the basis for transdisciplinary research in postindustrial contexts, as a platform for linking research in the academy to urban decision-
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".