Bibliographic record
Abstract
Abstract Archaeological data simultaneously serve as formal documentary evidence that supports and legitimizes chains of analytical inference and as communicative media that bind together scholarly activities distributed across time, place, and social circumstance. This contributes to a sense of “epistemic anxiety,” whereby archaeologists require that data be objective and decisive to support computational analysis but also intuitively understand data to be subjective and situated based on their own experiences as participants in an archaeological community of practice. In this article, I present observations of and elicitations about archaeological practices relating to the constitution and transformation of data in three cases in order to articulate this tension and document how archaeologists cope with it. I found that archaeologists rely on a wide variety of situated representations of archaeological experiences – which are either not recorded at all or occupy entirely separate and unpublished data streams – to make sense of more formal records. This undervalued information is crucial for ensuring that relatively local, bounded, and private collaborative ties may be extended beyond the scope of a project and, therefore, should be given more attention as we continue to develop open data infrastructures.
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 | 0.070 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.014 | 0.060 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".