Bibliographic record
Abstract
With this brief postscript, we want to take a moment to reflect on this milestone in the ongoing journey of Disrupting interviews and consider where we might go next. As many authors in this special issue shared, the process of a Disrupting interview itself carves out a space—an ethical space—for reflection on one's own complicity and responsibilities around challenging oppression and injustice within institutions, disciplines, teaching spaces, and oneself. The process of writing these chapters was, for many authors, another chance to reflect and further engage with the learning journey this method can provoke. The process of collaborating on this special issue has opened co-editors, associate editors, and contributing authors to the possibilities and differences that Disrupting interviews present. Joan noted the way that her approach emphasizes pedagogical applications with instructors, while Michelle, Lee, and Gabrielle focus on building an ethical community space where theory and practice blend. Gabrielle and Robin exchanged understandings around what a medicine walk could entail and signify in distinct Indigenous nations and individuals. Over the multi-year span of the project, we all noted how local, national, and global news events impacted conversations within and beyond Disrupting interviews (for instance, the ability of facilitators to name decolonization, reconciliation, Indigenization, diversity, equity, inclusion, and anti-racism as driving principles of the work, given shifting national conversations on these topics). In the invited reflective submissions, our use of a collegial peer review process allowed for an element of community building and sharing across Disrupting interview sites, and among individual participants who may not have realized they were part of a collective. Many authors shared appreciation for the opportunity to learn about what other instructors experienced in their Disrupting interviews, including how the process impacted them professionally and personally, and shared that collaborating in this special issue helped them further process and expand on their original interviews. As we mark this milestone in the Disrupting interview journey, we wonder where the project will go next. Will more people join the work, will more sites emerge? Will some collaborations come to a natural conclusion? What new adaptations will be developed? Will we host a conference, an institute, a gathering? Yet we also want to be open to the possibility that we do not know where we are going. The process of completing a Disrupting interview is full of choices and decisions, roadblocks and gates, pathways that are taken, pathways that are avoided or abandoned, pathways that are closed or barricaded or that only appear to be so. The uncertainty and the unknowing are a part of the process of Disrupting interviews themselves, and so we expect uncertainty and unknowing to also be a part of the project's future. We look forward to whatever answers come our way and hope that you will help us to find them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".