A Funny Thing Happened on the Way to Studying Portuguese Ethnic Media in Montreal: ‘Autosiociobiographie,’ Sibling Relations, Brotherly Love and ‘Emotional Episodes’ in Research
Bibliographic record
Abstract
How does the dynamic of sibling relations affect researching and dissemination of research findings? How can we best capture the unfolding of this dimension in researching? We discuss these questions via the description of a community event where the siblings were directly implicated and an “emotional episode” arose leading to humor in one sense but also stress on the other as the younger sibling struggled to intervene and interrupt his older brother’s presentation. We discuss and analyze this “key emotional episode” by placing it in the cultural upbringing of the siblings involved in the study as well as co-authors’ reactions to the same episode. We conclude by stating the necessity to know your audience and the approach most conducive to fit the culture of community engagement. Further, we argue that in this time when so much emphasis is placed on team research, we know so much about the academic credentials and skills of our project team members, but little about their personal upbringing and how this can influence relationships and interpretations of findings.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".