Archaeological Knowledge Production and Global Communities: Boundaries and Structure of the Field
Bibliographic record
Abstract
Abstract Archaeology and material cultural heritage enjoys a particular status as a form of heritage that, capturing the public imagination, has become the locus for the expression and negotiation of regional, national, and intra-national cultural identities. One important question is: why and how do contemporary people engage with archaeological heritage objects, artefacts, information or knowledge outside the realm of an professional, academically-based archaeology? This question is investigated here from the perspective of theoretical considerations based on Yuri Lotman’s semiosphere theory, which helps to describe the connections between the centre and peripheries of professional archaeology as sign structures. The centre may be defined according to prevalent scientific paradigms, while periphery in the space of creolisation in which, through interactions with other culturally more distant sign structures, archaeology-related nonprofessional communities emerge. On the basis of these considerations, we use collocation analysis on representative English language corpora to outline the structure of the field of archaeology-related nonprofessional communities, identify salient creolised peripheral spaces and archaeology-related practices, and develop a framework for further investigation of archaeological knowledge production and reuse in the context of global archaeology.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".