Bibliographic record
Abstract
The core purpose of accrediting educational credentials is to establish their conformity with standards established for educational credentials in general, particularly those offered by other institutions and in other fields. Educational accreditation integrates educational credentials within a network of all other educational credentials and their processes for assuring standards and quality. These processes are essentially conservative, being designed to minimise the risk of a failure of standards or quality. There are also pragmatic obstacles to recording multiple credentials from different sources within education’s accreditation system. In contrast, the recognition of expertise in employment is embedded within employment. The core criterion for the recognition of expertise in employment is the practitioner’s integration within a specific field of practice if not a site of employment. Comparability and still less similarity of practice with other fields and sites is irrelevant to the recognition of expertise in employment. Inasmuch as micro credentials seek to develop employability they are markedly different from programs that develop educational knowledge and skills. While such micro credentials may be recognised in employment, they seem incompatible with educational accreditation. The little evidence available is that micro credentials do not have strong employment outcomes. Micro credentials seem unlikely to address inequality in higher education which reflects deep and pervasive inequalities in society, and seem unlikely to strengthen links between education and work which depends as much on the structure of work and the labour market, and the cognitive content of jobs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.021 |
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".