Ecological Examination of Mortality Rate in an Infant Industry: The Roles of Legitimacy and Illegitimacy
Bibliographic record
Abstract
Population ecologists have sidestepped infant industries. Moreover, prior examinations have overlooked the level issue of legitimacy and the role of illegitimacy in firm failure. We suggest that both legitimacy and illegitimacy are potent antecedents of firm failures in an infant industry. We separate industry-level legitimacy from firm-level legitimacy and propose a novel “one-stage model.” This model indicates that incumbents of the infant industry concurrently take actions to advertise their typical and atypical firm features to industry spectators. These actions not only elevate both the industry-level legitimacy of the infant industry and the firm-level legitimacy of the incumbents but also simultaneously incite competition among the incumbents. We used a manually collected database of news articles on Chinese bicycle-sharing companies to examine firm failures in this infant industry from 2014 to 2017. We found that at the industry level, while industry-level legitimacy reduces a firm’s mortality, industry-level illegitimacy elevates the firm’s mortality. At the firm level, we confirm both the detrimental and beneficial effects of interfirm competition. When the rivals of the focal firm tout their atypical firm features, the focal firm’s likelihood of failure increases; when rivals and focal firm try to highlight their typical firm features, the focal firm’s failure rate decreases. When it comes to firm-level illegitimacy, both the focal firm and its rivals’ illegitimacies increase a firm’s mortality. We confirm that legitimacy and illegitimacy are not two poles of a single continuum.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".