Bibliographic record
Abstract
Dear Students, welcome to the wonderful world of archaeology! You might ask yourself, ‘Why am I learning about the past and why is my professor so excited to talk about stone tools, broken pottery, animal bones, or whatever other artifact excites them?’ Let us first start by introducing ourselves, and then hopefully we can help answer some of these questions. We (the editors) are all archaeology professors, just like your professor who told you to read this book. Michael and Katherine work at the University of Toronto (Ontario, Canada), while Danielle works at the University of Tulsa (Oklahoma, USA). We’ve known each other for more than 20 years. As professors, we’ve worked with generations of students in our classrooms, trying to excite the same passion for the past in our students as we have. However, sometimes this is challenging. As professors, we often struggle to communicate why students should care about something that happened thousands of years ago, and why it matters to the present. We will often talk at great lengths about artifacts, showing endless pictures of projectile points or different painted pieces of pottery, without actually showing students how to analyze them, or how to interpret the data we collect from them. We realized that if we want to engage students with the study of the past, we have to engage them in the present, in the classroom, and teach through doing , rather than showing. It is from these realizations that the idea for this book was born. In Learning Archaeology , we bring the voices of different archaeologists to you, providing real-world case studies with hands-on learning activities to allow you to analyze and interpret archaeological data.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.040 |
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".