Analysing micro-credentials in higher education: a Bernsteinian analysis
Bibliographic record
Abstract
This paper critiques the emergence of micro-credentials in higher education. It argues that micro-credentials build on the discourse of employability skills and 21st century skills within human capital theory, and that they increase the potential of human capital theory to ‘discipline’ the HE curriculum to align it more closely with putative labour market requirements. The paper is situated within the social realist school in the sociology of education, and it draws primarily on the sociology of Basil Bernstein to develop this critique, while also drawing on the Continental Didaktik tradition. It analyses the nature of the person envisaged in curriculum, the homo economicus of human capital theory. This self is a market self who uses micro-credentials to invest in this or that set of skills in anticipating labour market requirements. The paper uses a range of Bernstein’s concepts to analyse the links between what is to be taught, to whom is it taught, and how is it taught in micro-credentials. It focuses on the principle of recontextualization which comprises instructional and regulative discourses, to examine the ways in which notions of the person and human motivation are reshaping relations of classification and framing in HE curriculum.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".