Interview with the Institute of Archaeology’s new Director: Professor Kevin MacDonald
Bibliographic record
Abstract
Professor Kevin MacDonald was appointed into the role, beginning in September 2022.Here he summarises his career to date, together with his vision for the future of the Department in both the short and longer terms. What brought you into archaeology?Like most of us, I suppose there were a number of factors that led me into the discipline.My father was from a family of Newfoundland fishermen and merchant sailors.My uncle and great uncles had travelled the world at sea and I grew up listening to them spinning yarns about West Africa, South America and East Asia -so from the start I was eager to travel.Then my Dad was Comptroller [Financial Manager] for the Moody Foundation in Galveston, Texas.This was at the high point of the historic buildings preservation movement and the foundation was very involved in that.Most weekends I was out with him visiting one restoration project or another -nineteenth-century buildings, steam trains, even the tall ship Elissa.That fed into my passion for history.When it came time for university -I was pushing to go to music conservatory -my parents
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.018 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".