CIO SURVIVAL AND THE COMPOSITION OF THE TOP MANAGEMENT TEAM
Bibliographic record
Abstract
We explore empirical regularities of CIO survivability in public and private organiza-tions using CIO job tenure durations spanning 1994 to 2009 for 1,594 executives. We employ the Kaplan-Meier estimator from event history analysis to compute survivor functions for CEOs, COOs, CFOs and CIOs. We make log rank comparisons of job tenure durations to make inferences within/across executive titles, and between public and private sector groups. The results suggest that: CIOs have shorter survival durations than CEOs and COOs, comparable to CFOs; private sector CIOs have longer durations than public sector CIOs; CIO membership in the top management team increases sur-vivability; female CIOs stay a shorter time than males; and women on the top man-agement team diminish CIO tenures overall. From an additional executive arrival and departure time proximity analysis, we find that only a quarter of CIOs are members of the top management team, but membership lengthens tenure.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".