An exploration of Tajfel's Social Identity Theory and its application to understanding Métis as a social identity
Bibliographic record
Abstract
Abstract\nThis thesis explores Henri Tajfel’s Social Identity Theory (1981) with a specific focus on the process of self-categorization. Tajfel’s theory provides the theoretical framework to understand the social category of Métis as a social group. Eight self-identified Métis adults were interviewed individually utilizing a semi-structured interview to explore their Métis self-identification and operationalize the conceptual framework. The three main research questions used to develop the conceptual framework are: 1) what are Métis characteristics?, 2) Do self-identified Métis adults evaluate the Métis group to which they identify as positive, negative or both?, 3) Do self-identified Métis adults feel like they fit in or belong to the Métis group? Both open ended and closed ended questions were used to explore Métis adults’ perspectives related to their social self-categorization. Data were analyzed; conclusions were drawn and verified utilizing the recommendations of Miles and Huberman (1994). Findings were theoretically interpreted utilizing the social identity perspective. The study’s results support the use of Tajfel’s theoretical conception of a group as a conceptual framework in understanding the experience and perspective of the Métis participants in this study.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".