See who I know! Addressing the liabilities of outsidership through status signaling
Bibliographic record
Abstract
Abstract Status is an important intangible asset, yet when firms enter new countries, they lack standing in new social hierarchies as outsiders. Conventional wisdom suggests embeddedness in host-country networks can alleviate newcomers’ liability of outsidership. We complement this with insights from status signaling theory: Newcomers in host countries can address their liability of outsidership through the visual display of social affiliations. We utilize a novel visual qualitative research approach that analyzes annual report photographs of an emerging-market family business, depicting the firm’s leaders with high-status alters. Complementing the international business literature, which emphasizes strong ties to host-country business partners, we identify three signaling mechanisms that are more circuitous: bypass (host-country affiliations beyond the firm’s industry), allusion (global affiliations beyond the host country and industry, often celebrities), and aspiration (global industry affiliations). We also suggest that such diffuse status signaling mechanisms may be especially salient in emerging-market family firms investing in developed markets, which are accorded low status in many developed markets. These firms feature firm/owner identity overlaps, long leader tenures, and a tendency to build reputation through prosocial behavior, facilitating the activation of status signaling tactics through ephemeral affiliations with high-status actors situated in world society.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".