Introduction: Current Challenges and New Directions in Lithic Analysis
Bibliographic record
Abstract
The Lithics and Technology Group at the University of Toronto called together any and all graduate students that focus on lithic material to attend the first ever Graduate Students Lithics Symposium in February, 2010. The event served as a venue for graduate students to share their current research methodologies with their peers interested in lithic technology studies. The goal was to facilitate scientific exchange, generate feedback, questions, and discussions that would help to advance innovative research. The title was “Between a Rock and a Hard Place”, emphasising the many challenges faced by lithic analysts today. It was suggested that one of the most important challenges we face is the incorporation of new methods and techniques such as spatial analysis, residue and use-wear analysis, raw material sourcing, digital imaging and 3D scanning into our analyses, not as an end in itself, but as an integral step towards addressing the “big questions” of human interaction, exchange, technology, and meaning. Eighteen students from the archaeology and anthropology departments of the University of Toronto, McGill University, Simon Fraser University, Arizona State University, University of Connecticut, University of Alberta, University of Victoria, McMaster University, and University of Western Ontario attended the symposium. In addition to the traditional conference-style presentations, the two- day event also included a lithics and data “show-and-tell” and a round-table discussion on the present and future state of lithic studies.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".