Bibliographic record
Abstract
Lisa Smirl (1975–2013) was a scholar of international relations and former aid worker who brought a deep understanding of the business and practice of aid alongside a rich appreciation of theories and concepts to make sense of them. Her life was ended by cancer a few years after completing her PhD and taking up a lectureship at the University of Sussex (UK). Despite the brevity of her academic career, she had acquired a burgeoning reputation amongst colleagues studying humanitarian aid, conflict resolution and post-conflict reconstruction. Lisa was fascinated by the social world of aid and how its objects and spaces shaped humanitarian and development practice. Many of the questions which Lisa addressed in her academic work are now the subject of lively inter-disciplinary debate across anthropology, sociology, development studies and political science. This set of papers by contributors to a workshop as part of the International Studies Association annual convention held in Toronto in March 2014 is a direct engagement with Lisa's work and constitutes a series of studies of the objects and spaces of international aid. One of the papers is a posthumously published paper of Lisa's whilst the other three papers are written by peers who worked with and knew Lisa during her career.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".