Bibliographic record
Abstract
Besides introducing this Cluster Event of 27-27 March 2013, I want to use this opportunity to say a few words about the history and potential future of the Population Change and Lifecourse Strategic Knowledge Cluster.I will also offer some comments on conferences involving academic and public sector researchers. History of the ClusterThis history goes back to the Social Science and Humanities Research Council (SSHRC) competition for Strategic Research Cluster Design Grants (2004-05) and the subsequent Completion Grant .Notice that these were called "research clusters" and that the basic idea was to help SSHRC design the appropriate format for these clusters.SSHRC brought together the 31 successful applicants for Cluster Design Grants on 18 February 2005, in an occasion called "Designing the ideal cluster."Besides the Cluster Design Grants, other large SSHRC grants were invited to this occasion, with each having a poster on exhibit.This provided an opportunity for possible collaboration across groups, and networking with potential partners.At this occasion, Marc Fonda of SSHRC, Jean-Pierre Voyer of Policy Research Initiative, and Peter Hicks of Human Resources and Social Development Canada, proposed that three of the groups collaborate under an umbrella called "Population
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.175 | 0.119 |
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".