Conceptualising community engagement as an infinite game implemented through finite games of ‘research’, ‘community organising’ and ‘knowledge mobilisation’
Bibliographic record
Abstract
Meaningful community engagement process involves focusing on the community needs, building community capacity and employing culturally tailored and community-specific strategies. In the current practices of community-engaged health and wellness research, generally, community engagement activities commence with the beginning of a particular research project on a specific topic and end with the completion of the project. The outcomes of the community engagement, including the trust, partnership and contribution of the community to research, thus remain limited to that specific project and are not generally transferred and fostered further to the following project on a different topic. In this viewpoint article, we discussed a philosophical approach to community engagement that proposes to juxtapose community engagement for the specific short-term research project and the overarching long-term programme of research with the finite game and infinite game concepts, respectively. A finite game is a concept of a game where the players are known, rules are fixed and when the agreed-upon goal is achieved, the game ends. On the other hand, in infinite games, the players may be both known and unknown, have no externally fixed rules and have the objective of continuing the game beyond a particular research project. We believe community engagement needs to be conducted as an infinite game that is, at the programme of research level, where the goal of the respective activities is not to complete a research project but to successfully engage the community itself is the goal. While conducting various research projects, that is, finite games, the researchers need to keep an infinite game mindset throughout, which includes working with the community for a just cause, building trust and community capacity to maximise their contribution to research, prioritising community needs and having the courage to lead the community if need be. Patient or Public Contribution: While preparing this manuscript, we have partnered actively with community champions, activists, community scholars and citizen researchers at the community level from the very beginning. We had regular interactions with them to get their valuable and insightful inputs in shaping our reflections. Their involvement as coauthors in this paper also provided a learning opportunity for them and facilitated them to gain insight on knowledge engagement. All authors support greater community/citizen/public involvement in research in an equitable manner.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".