Campus Emergency Managers
United States · BLS Employment Projections 2024–34: this SOC code is not among the published items.
AI exposure in published research
Figures below are reproduced from external datasets without modification. Where a dataset does not cover this occupation, the value is shown as — rather than as zero.
Data provider: OpenAI · "GPTs are GPTs"
Time basis: 2023 baselineex-ante estimate
Scored against GPT-4-generation capability.
- Human rater basis
- —
- GPT-4 rater basis
- —
This occupation code is not included in this dataset.
This occupation code is not included in this dataset.
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
— This occupation code is not included in this dataset.
- License:
- MIT License · Copyright (c) 2024 OpenAI
Data provider: Anthropic Economic Index
Time basis: Varies by releasecomposite index
- Observed exposure
- —
This occupation code is not included in this dataset.
Theoretical exposure index weighted by measured Claude usage, per the original report's definition.
— This occupation code is not included in this dataset.
Data: Anthropic Economic Index — labor_market_impacts, CC-BY, https://huggingface.co/datasets/Anthropic/EconomicIndex
AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
- License:
- CC BY 4.0
Theoretical estimate vs observed usage
One card (GPTs are GPTs) estimates what AI could theoretically do at the time it was scored; the other (Anthropic Economic Index) observes how AI was actually used. They measure different things, so the two figures cannot be added, averaged, or ranked against each other. The older figure is kept here as a baseline for comparison rather than removed.
Caution: the Anthropic figures — observed exposure and task penetration — take the Eloundou β as one of their inputs. The two sides resembling each other is therefore not evidence that the earlier prediction came true; reading it that way is circular reasoning.
The Anthropic figures are measured on Claude users, who are not the whole economy and not the whole workforce.
The mapping of O*NET tasks and occupation codes was performed by AI Changing Work. The source figures themselves were not modified.
These indices are not forecasts. Which point in time each one belongs to is stated on the badge on its card.
Task Breakdown
- Draft and update emergency response plans
- Conduct emergency drills and training exercises
- Analyze threat assessments and incident data
About This Occupation
If you work as a Campus Emergency Manager, AI is augmenting your planning and analytical tasks. With an automation risk of 18/100 and overall exposure at 40%, this role faces medium transformation. Threat data analysis sees the highest automation at 62%. BLS projects +6% growth through 2034.
ISCO-08 classification
Professional Services Managers Not Elsewhere Classified
Indirect mapping — this occupation is linked to the ISCO unit group by a rule-based fallback.
Definition
ILO original text (English)
This unit group covers managers who plan, direct coordinate and evaluate the provision of specialized professional and technical services and are not classified in Minor Group 121: Business Services and Administration Managers, or elsewhere in Minor Group 134: Professional Services Managers. For instance, managers responsible for the provision of policing, corrective, library, legal and fire services are classified here.
Definition & vocabulary source
Source: International Labour Organization (ILO) — ISCO-08 Structure
License: ILO CC BY 4.0
Official occupational information
- ONET11-9111.00
Medical and Health Services Managers
Plan, direct, or coordinate medical and health services in hospitals, clinics, managed care organizations, public health agencies, or similar organizations.
View original - ONET11-9199.11
Brownfield Redevelopment Specialists and Site Managers
Plan and direct cleanup and redevelopment of contaminated properties for reuse. Does not include properties sufficiently contaminated to qualify as Superfund sites.
View original
Frequently Asked Questions
They come from two published datasets: the Anthropic Economic Index (labor_market_impacts, CC BY 4.0) and the OpenAI "GPTs are GPTs" exposure rubric (MIT License, Copyright (c) 2024 OpenAI). AI Changing Work maps them onto O*NET occupation and task codes and does not calculate exposure scores of its own. AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
No. They are a diagnosis of exposure as measured at the time each source dataset was published. AI Changing Work publishes no prediction of future automation or job displacement for this occupation.