Research & Publications
What we publish.
What we publish.
What we cite.
What is forthcoming.
The Career Intelligence Framework is grounded in five published research streams. Our publications document the methodology, the construct development, and the empirical foundations — and they are open to peer review. This page is an honest account of what is available now and what is in preparation.
Publications
What is available now.
Framework paper
The Career Intelligence Framework: A six-pillar model for career capability in an AI-shaped economy (v1.0)
Request access
The foundational document of the Career Intelligence Framework. Documents the six-pillar construct structure, the five research streams that underpin it, the operationalisation of each pillar into measurable dimensions, and the scoring logic behind the Career Scan diagnostic. Written to doctoral research standards and prepared for peer review.
Request a copy →
Validation document
Judgment Lab — An AI-coached simulation platform for building the decision-making and judgment that AI cannot replace
Request access
The concept and validation case for the Judgment Lab product. Documents the develop-first positioning, the operations beachhead rationale, the 10 seed scenarios, the 8-capability scoring framework (grounded in the Diamond executive function model and WEF durable skills research), and the four-phase validation roadmap with kill criteria.
Judgment Lab overview →
White paper
Career Intelligence for the South African Graduate: AI exposure, employability, and the six-pillar model
Forthcoming
Applies the Career Intelligence Framework to the South African graduate context — drawing on Bezuidenhout (2010) and Coetzee (2014) alongside Acemoglu-Restrepo (2022) to map AI exposure across SA graduate employment sectors. Intended for university careers services, DHET, and SETA audiences.
Research report
AI Opportunity Mapping: Field-specific demand signals across 23 South African professional disciplines
Forthcoming
Documents the AI Opportunity Layer methodology — how demand signals are constructed, which data sources are used, how they are mapped to the six CI pillars, and what the signals say about each of the 23 fields covered by the Academy. Intended for researchers and institutional partners.
Research foundations
The five streams
The five streams
we build from.
We do not invent the theoretical foundations of the Career Intelligence Framework — we integrate and extend five published bodies of scholarship. The citations below are precisely what they are: the sources of the architecture, not decorative references.
If you are using the framework in your own research, these are the primary citations. Cite both the originating scholars and the PositionMeAI integration.
Stream 01
Career construction theory — Savickas (2013)
Savickas, M.L. (2013). Career construction theory and practice. In Career development and counseling (2nd ed., pp. 147–183). Wiley.
Theoretical foundation for Pillar 06 (Navigation). Career adaptability, self-directedness, and long-arc resilience.
Stream 02
Graduate Skills and Attributes Scale — Coetzee (2014)
Coetzee, M. (2014). Measuring student graduateness. Higher Education Research & Development, 33(5), 887–902.
Primary measurement scaffold for the Career Scan. GSAS construct structure adapted for AI labour market context. Informs Pillars 01–04.
Stream 03
SA graduate employability — Bezuidenhout (2010)
Bezuidenhout, M.L. (2010). The development of a measure of graduate employability in the context of the new world of work. UNISA.
Grounds the framework in the South African professional context. Field-specific employability attribute adaptation.
Stream 04
AI and labour markets — Acemoglu & Restrepo (2022)
Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in US wage inequality. Econometrica, 90(5), 1973–2016.
Task-based model of automation underlies the AI Opportunity Layer demand signal architecture. Informs Pillars 01–02.
Stream 05
Occupational AI exposure — Anthropic Economic Index (2025)
Anthropic. (2025). The Anthropic Economic Index: AI's impact on the economy and labor markets. Anthropic.
Empirical foundation for field-specific AI capability mapping. Informs Pillar 03 (Capability) and the Academy curriculum structure.
Research agenda
Make contact →
What we are working on.
The following research questions are active in our work. Researchers interested in collaboration on any of these areas are welcome to make contact.
01
Longitudinal CI development in South African graduates
Does Career Intelligence as measured by the Career Scan change meaningfully over a 12-month period when learners engage with the Academy? What predicts change?
02
Judgment Lab capability scoring validity
Can LLM-based evaluation of reasoning quality in simulation scenarios produce reliable, reproducible scores across the 8 capability dimensions? What scoring rubric design achieves the highest inter-rater reliability?
03
AI exposure differentials across SA professional disciplines
Using the Acemoglu-Restrepo task model applied to SA occupational data, how does AI exposure vary across the 23 disciplines in the Academy, and how does this map onto graduate employment outcomes?
04
Career Intelligence as a predictor of AI adoption
Do professionals with higher CI pillar scores (particularly Capability and Strategy) show faster and more effective AI tool adoption in their roles? Can the Career Scan baseline predict this?
Interested in collaborative research?
We are open to collaboration with researchers in career development, I/O psychology, labour economics, and AI capability assessment.
Research updates
New publications and research notes
as they are released.
as they are released.
We publish infrequently and never speculatively. When a new paper, white paper, or methodology note is available, it goes to this list first. No marketing. One email per publication.