Making Sense of Belonging: Analytical Pathways, Bias Corrections, and What Statistics Can (and Cannot) Tell Us about Inclusion.
Bibliographic record
Abstract
This methodological companion piece provides a practical and detailed account of the analytical choices underpinning my article Societal Factors Influencing the Sense of National Belonging: A Statistical Analysis of Residents in Canada (Keiff, 2025). Drawing on a nationally representative dataset, the guide reconstructs the step-by-step process of building a composite index of belonging using factor analysis, distinguishing between territorial and community anchors. It then presents the construction of an intersectional discrimination score via logistic regression, designed to capture the cumulative and intersecting nature of exclusion. It discusses how the analysis addresses selection bias, particularly the counterintuitive finding of lower belonging among highly educated individuals, through an instrumental variable approach. Throughout, the text explains not only the technical procedures but also the theoretical assumptions, limits, and interpretive challenges at each stage. It engages explicitly with methodological debates such as the “Table 2 fallacy” and the interpretation of statistical significance. Beyond the technical details, this guide demonstrates how quantitative models both reveal and obscure social structures and invites readers to consider the broader implications for public debate and inclusion policy. The aim is to provide a transparent, reproducible resource for those wishing to understand, critique, or extend the analytical pathway followed in the original article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.215 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".