A Day in the Life of Archaeological Services Inc. (Ontario, Canada)
Bibliographic record
Abstract
This resource is a single blog post created as part of the Day of Archaeology initiative. The Day of Archaeology project aimed to provide a window into the daily lives of archaeologists from all over the world. The project asked people working, studying or volunteering in the archaeological world to participate in a 'Day of Archaeology' each year by recording their day and sharing it through text, images or video on the Day of Archaeology blog. The project asked anyone with a personal, professional or voluntary interest in archaeology to get involved, and help highlight the reasons why archaeology is vital to protect the past and inform our futures. The resulting Day of Archaeology project archive demonstrates the wide variety of work the archaeological profession undertakes day-to-day across the globe, and helps to raise public awareness of the relevance and importance of archaeology to the modern world. The first ever Day of Archaeology was held in 2011 and, since then, the project had thousands of archaeologists take part in the event, from those working in the field through to specialists working in laboratories and behind computers. The last Day of Archaeology was on Friday 28th July 2017.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.225 | 0.058 |
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".