Bibliographic record
Abstract
Andrea Harrington is a licensed attorney in the Commonwealth of Massachusetts and an Erin J.C. Arsenault Fellow in Space Governance at the McGill University Institute of Air and Space Law, where her doctoral research is focused on insurance and liability issues for the commercial space industry. Andrea holds an LLM, also from the McGill IASL, as well as a JD from the University of Connecticut School of Law, an MSc from the London School of Economics, and a BA from Boston University. During the course of her studies at McGill, Andrea has earned numerous awards, including: a P.E.O. Scholar Award, the International Aviation Women’s Association Scholarship, the SWF Young Professionals IAC Scholarship, and the Setsuko Ushioda-Aoki Prize for academic merit. Andrea has served as an Assistant Editor for the Annals of Air and Space Law, Jr. Project Manager for Secure World Foundation, and researcher on projects for the FAA Center of Excellence for Commercial Space Transportation, the International Society for the Advancement of Space Safety, the International Civil Aviation Organization, and the Space Security Index; prior to seeking her doctorate, Andrea obtained a combined 5+ years of experience in the insurance and financial compliance fields.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.087 | 0.036 |
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".