Tales of Hoards and Swordfighters in Early Bronze Age Scandinavia: The Brand New and the Broken
Bibliographic record
Abstract
This article focuses on the complexity of Early Bronze Age weapon depositions. While some of the deposited weapons have been disabled by intentional breakage, others seem to be more or less unused. A plausible explanation for the variability is that the surrender of lethal weapons to land or water was a means of coping with their power or agency – their individuality. We suggest that weapons, in their capacity as extensions of warriors’ bodies, may have substituted for humans in ritual depositions. The metalworkers also come into play, due to their capacities in the processes of making weapons and shaping weapon technologies. Although we consider the three depositions that we discuss to relate to rituals on the occasion of warfare, we are not aiming for a uniform explanation. In the same way as the patterned human behaviour of a ritual is a means of subsuming individual events into a greater order, so a focus on general patterns may subsume the complexity of the past by ignoring the many different events leading to, e.g., the deposition of metalwork. Far from seeing these perspectives as contradictory, we try to use three well-documented individual cases to shed light on the variability within the pattern.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".