Racial identity, racial attitudes, and race socialization among Black Canadian parents.
Bibliographic record
Abstract
The primary aim of this study was to examine the influence of racial identity on the socialization strategies used by Black parents to deal with issues of racism and discrimination. The Multidimensional Model of Racial Identity (MMRI) was used to capture the complexity of Black identity and to provide a framework for the study of the socialization process. Ninety-one Black Canadian parents responded to measures of racial identity (e.g., identity centrality, racial ideologies), racial appraisals (e.g., concern for stereotyping), and socialization practices (e.g., preparation for bias). Racial identity measures were hypothesised to predict racial appraisals and socialization behaviours, while racial appraisals were expected to predict socialization behaviours. Furthermore, racial salience was expected to moderate the relationship between racial ideologies (e.g., nationalist ideology) and socialization behaviours. Although this latter hypothesis was not supported, the Sellers model did provide a useful theoretical framework for understanding the socialization practices of Black Canadian parents. Parents were more likely to socialize their children when they endorsed a humanist ideology and when they perceived their children as being likely targets of stereotyping and discrimination. These findings underscore the need for multidimensional measures of identity to obtain a more complete picture of the socialization process.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".