Bibliographic record
Abstract
This book is the result of an encounter between a heterogeneous group of social scientists and the Canadian Institute for Advanced Research (CIFAR). This innovative research organization has a well-established practice of supporting the work of researchers over several years so that they can engage in interdisciplinary exploration of new and important topics. Unlike other funding organizations, CIFAR gives its researchers carte blanche. It does not require a predefined plan with clear deliverables. It recognizes the open-ended nature of the research process and aims to facilitate and empower it. This highly original approach often leads to unexpected results. In 2002, some of us were contacted by CIFAR and asked to come together to think about what defines successful societies and the social conditions that sustain them. After supporting research teams in the fields of population health and human development for a decade, CIFAR was turning its efforts in a new direction to consider a wider range of social factors affecting population health. It called upon us to bring to the table the analytical tools we had deployed in our respective research on a range of topics, including the impact of institutions and cultural frameworks on social relations. Thus an interdisciplinary team that included sociologists, political scientists, a historian, an epidemiologist, and a psychologist came together. We met several times a year in various locations to exchange papers, to learn from each others' work, and to interact with other scholars.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.348 | 0.184 |
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".