Invited Paper. The Hermeneutic Wager: Building Community in Pediatric Neuro-Oncology
Bibliographic record
Abstract
During the Covid-19 pandemic, Hovey was contacted by the lead of a pan-Canadian working group on pediatric brain tumours (PBTWG). While all stakeholders (researchers, clinicians, regulators, patient advocates, ethicists, and industry experts) were highly motivated to address barriers through innovative strategies in collaboration, clinical research, regulation, and business models, advancement has been challenging on multiple levels. Hovey and his team were tasked to facilitate and successfully engage this diverse divisive group of stakeholders to achieve their goals. Inspired by Richard Kearney’s anatheistic wager, the hermeneutic wager acts simultaneously as a team building and research approach, as it serves to gain insight into the perspectives of members of a purposeful community. Through its five conversations, namely imagination, humility, commitment, discernment, and hospitality, the hermeneutic wager elicits responses from the participants that are based on meaningful participation in a relational approach of community co-creation. We individually interviewed the PBTWG facilitators (5). With informed consent, our research team also recorded all 5 of the PBTWG work group meetings (20 participants from 6 stakeholder groups) and break-out room meetings and took notes which consist of rich and extensive narrative data. This data was analyzed alongside the individual PBTWG interviews. The audio and visual data collected via a secure Zoom platform was then transcribed verbatim and analyzed interpretively according to the applied philosophical hermeneutic tradition. Findings centered around six points: “The Work of Stories,” “Changing Landscapes: Community / Communication not Consensus,” “Let the Words Lead You,” “Those Words Matter,” “Metaphors as a Bridge to Understanding,” and “A Road Map to be Inspired By.” Through these findings, we contend that the hermeneutic wager is an invitation for conversation that builds a path to the generation of new and creative understandings that transcend previous ways of knowing. The efficacy of the hermeneutic wager resides in its ability to help build a community of people who work together through and across difference to arrive at a shared understanding and collective outcome.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".