National Identity, National Favoritism, Global Self-Esteem, Tall Poppy Attitudes, and Value Priorities in Australian and Canadian Samples
Bibliographic record
Abstract
This study compared samples of Australian and Canadian university students with respect to attitudes toward tall poppies or high achievers, national favoritism as indicated by a tendency to favor the products and achievements of their own country, global self-esteem at the personal level, and value priorities. Measures of national identity and national identification were also obtained for the Australian sample. Results showed that both samples were similar in regard to national favoritism, tall poppy attitudes, and global self-esteem. In both samples, favoring the fall of tall poppies was negatively related to both self-esteem and national favoritism, and national favoritism was positively related to self-esteem. National favoritism and national identification were positively related in the Australian sample. Categorizing self as Australian rather than having some other national identity was associated with higher national favoritism and higher national identification, consistent with social identity theory. National favoritism scores were higher for Canadian Anglophones than for Canadian non-Anglophones. Australian students rated some universalistic, prosocial values such as equality, a world of peace, and a world of beauty as more important for self and conformity values as less important when compared with the Canadian students. Gender differences in value ratings were consistent with previous findings.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".