Exploring Connections Between Existing Theoretical Frames and Methods in the Study of Everyday Cohabitation: Notes from Two Research Projects
Bibliographic record
Abstract
In a social and political context marked by increasingly polarized attitudes towards immigration and ethnic diversity, the paper uses the results of two research projects conducted ten years apart in Brussels and Montréal to reflect on methodological issues. The paper discusses two themes to show how to engage methodologically within a changing context, both theoretically and politically, and how various methodological choices might be used to fill some knowledge gaps. The first theme concerns the choice of research methods, and the type of knowledge they produce. The second theme addressed in this paper stresses the importance of paying more attention to the context where the research takes place, a process denoted here by the term situating. Two different interview methods are central to the discussion: in-depth interviews and ‘on-the-spot’ short interviews. The paper contrasts their use in relation with each project’s research objectives.
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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 | 0.060 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.026 | 0.057 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| 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".