Bibliographic record
Abstract
International Relations scholars concerned with explaining status-seeking behavior in the international system draw heavily from social comparison theory and its observations that individuals judge their worth, and accordingly derive self-esteem, through social comparisons with others. According to this logic, states become status seekers because, like individuals, they have an innate desire for favorable social status comparisons relative to their peers. Thus, the great power status literature is often framed in the language of accommodation, and adjustment, which presupposes that status insecurities develop from unfavorable social comparisons and can be resolved through relative social improvements. This article challenges these assumptions by noting, as psychology has acknowledged for some time, that individuals use both social and temporal forms of comparison when engaging in self-evaluation. Where social comparisons cause actors to ask “How do I rank relative to my peers?” temporal comparisons cause actors to evaluate how they have improved or declined over time. This article advances a temporal comparison theory of status-seeking behavior, suggesting that many of the signaling problems associated with status insecurity emerge from basic differences in how states evaluate their status, and whether they privilege temporal over social comparisons. The implications are explored through China’s contemporary struggle for status recognition, situating this struggle within the context of China’s civilizational past and ongoing dispute over Taiwan.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".