Bibliographic record
Abstract
The typologies that archaeologists use to classify artefacts and situate them chronologically and culturally are crucial tools of the discipline; when left unquestioned, however, they tend to produce reductive and essentializing understandings of the past. Like all theoretical interventions, assemblage theory questions the unquestioned, in this case, asking archaeologists to radically rethink the relationality of the world and the power and vibrancy of nonhuman and nonliving things like stone. In this article, we take an assemblage-based approach to an old typological problem – sorting birdstones. Since the mid-19th century, collectors and archaeologists categorized birdstones found throughout the American Northeast according to evolutionary or culture-historical principles. These approaches paid little attention to different varieties of stone, often regarding birdstones as if they were passive reflections of normative mindsets that came in only three culture-specific types. Here, we explore how archaeologists might ‘reassemble’ typological thought, analysing and thinking through a large sample of materially varied birdstones to find much more than three little birds. Recognizing how the shared and specific capacities of different stones actively contributed to the multiplicity of birdstone morphologies resituates them as singular and changing assemblages while highlighting the potentials of questioning the fixity of both typological and material categories at large.
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".