Diversity and Demographics of Zooarchaeologists: Results from a Digital Survey
Bibliographic record
Abstract
Nearly 25 years ago, a “Zooarchaeology Practitioner Survey” was distributed via conventional mail to individuals in the USA and Canada and received 122 responses over a period of several months in 1991. Now, a revised “Demographics in Zooarchaeology Survey” provides an update to those data and assesses the current state of the field. The 2014 survey remained open for 3 months and received 288 responses from practitioners worldwide. Global participation was made possible by hosting the survey online. Key findings of the 1991 survey included disparities in employment rank for women despite similar levels of degree level attainment as men, a point which the 2014 survey sought to investigate. This trend appears to persist for those without the PhD and at the highest levels of income for those holding a PhD. In addition, the recent survey asked participants about their racial or ethnic identity in order to evaluate the demographic diversity of the discipline beyond sex, age, and nationality. Data regarding topical and geographic research area were also collected and reflect a subtle bias towards working with mammals and a focus on research questions grounded in prehistory in Europe and North America, followed by Australia and Southwest Asia. Results are compared with those of the earlier survey and membership information from the International Council for Archaeozoology.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".