Strengths and limitations of the Inclusive Society research model: an autoethnography
Bibliographic record
Abstract
Purpose: The Inclusive Society partnership research model aims to promote change in society for people with disabilities by supporting research teams composed of researchers and partner organizations. The objective of this article is to identify the strengths and limitations of this research model.Material and methods: An autoethnography approach was used. Thematic analysis of four methods was undertaken: semi-directed interviews with members of the research teams funded by Inclusive Society (researchers, partners), a focus group with the Inclusive Society’s intersectoral collaboration agents, their logbooks, and Inclusive Society’s annual reports.Results: Strengths and limitations of the Inclusive Society model were identified through their networking activities, the role and support of the intersectoral collaboration agents and the partnership research program.Conclusions: Networking activities are an essential element of Inclusive Society. They are indispensable for composing intersectoral research teams that will work on answering needs of people with disabilities. Intersectoral collaboration agents are also a strength of the model, but their role could be clarified to better frame what tasks are in their scope of practice and what the research teams could ask from them. Finally, the research program eligibility criteria could be improved to support, among others, the projects’ appropriation phases.
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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 | 0.109 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".