Re-discovering Archaeological Discoveries. Experiments with reproducing archaeological survey analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article describes an attempt to reproduce the published analysis from three archaeological field-walking surveys by using datasets collected between 1990 and 2005 which are publicly available in digital format. The exact methodologies used to produce the analyses (diagrams, statistical analysis, maps, etc.) are often incomplete, leaving a gap between the dataset and the published report. By using the published descriptions to reconstruct how the outputs were manipulated, I expected to reproduce and corroborate the results. While these experiments highlight some successes, they also point to significant problems in reproducing an analysis at various stages, from reading the data to plotting the results. Consequently, this article proposes some guidance on how to increase the reproducibility of data in order to assist aspirations of refining results or methodology. Without a stronger emphasis on reproducibility, the published datasets may not be sufficient to confirm published results and the scientific process of self-correction is at risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it