Atergang i arbete: ett internationellt samproduktionsprojekt
Bibliographic record
Abstract
This chapter illustrates how the conditions for international, co-produced research can be made possible through an international education program at research level. The purpose of the joint collaborative project, which includes authors from Sweden, Canada, Australia and Belgium, demonstrates in this chapter, how to increase dissemination of knowledge by working together. By co-producing a text, based on the authors' individual studies, conducted in various welfare contexts, new knowledge can be created and raise more research questions in an area that was more or less invisible in the literature. An important prerequisite for co-producing research results has been the education program 'Work Disability Prevention CIHR Strategic Training Program'. It is an education program that is part of a common network that the authors participated in, based on international education for postgraduate students, postdocs and younger researchers, who have over 30 senior researchers connected to the network. The chapter begins with a description of the joint education program and the network. Furthermore, the results of co-production will be illustrated by a synthesis of the authors' individual research studies that they co-produced about the role of employees in a sick leave process. The chapter concludes with a discussion about what the profits and challenges of co-production in international research can be.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".