Old spaces, new places: legacy data and the spatial organization of Early Bronze III houses in the non-elite domestic quarter of Tell eṣ-Ṣâfi/Gath, Israel
Bibliographic record
Abstract
The goal of this thesis is to contribute to a more dynamic and holistic vision of the social complexities of early urban societies in the Near East by increasing the knowledge of intra-settlement household organization and variability. This thesis approaches the household from a materials perspective, using three specialist datasets to identify the boundaries of households and their continuity between phases. To conduct the analyses proposed for this thesis, a geographic information system (GIS) that integrates all of the excavation data from over a decade of field excavation was necessary to construct. This GIS spatial database enables data to be both stored and analyzed. The digitization of the site data and their integration are also vital in the examination of legacy data as is used in this thesis. The term ‘legacy data’ refers to any data that are from an obsolete information system. In the field of archaeology, this often translates to non-digital. The digitization and analysis of such data are theoretically possible for any site and allows for reexamination of the site after years of being archived. The process provides for the creation of an electronic database (where one may not have previously existed) that allows for renewed data access and addresses storage concerns. This thesis makes a substantive contribution to the understanding of early urban society in the southern Levant by approaching the dearth of research on Early Bronze Age households from a spatial analytic perspective. Data from Tell eṣ-Ṣâfi/Gath (Israel) are used to generate models of the spatial dynamics of households in this early urban center. A clearer understanding of generational continuity of habitation of architectural units, architectural units as a representation of households, use of space within architectural units, and household (domestic) level tasks provides information that is not accessible from top-down approaches. The use of legacy data in the analysis tests the feasibility of these types of analyses on data collected before digitization was widespread. This thesis tests whether the digitization process and subsequent analysis of legacy data are valuable and return meaningful results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".