Identifying typical academic language and learning development practitioner roles and specialisms: an international taxonomy
Bibliographic record
Abstract
Although the higher education ‘third space’ has become more widely recognised, there is still a prevailing lack of specificity in terms of many associated job roles. In contrast to librarians (CILIP, 2025), there is no formally recognised classification of types of Academic Language and/or Learning Development (ALLD) job roles. In practice, this means that ALLD practitioners with similar job titles often undertake different roles. In the absence of clearly defined job roles, the valuable contributions made by ALLD practitioners and the associated specialist skills and knowledge required are not always widely understood (Bickle, Johnson and White, 2024). This led Briggs (2025a) to propose the need to develop an ALLD role taxonomy. The current article reports results from an international study (primarily comprising of practitioners from UK, Canada, and New Zealand) that sought to establish the principal job responsibilities and specialisms synonymous with working in ALLD. Based on data from 92 respondents, it was possible to develop an ALLD practitioner taxonomy that details the most frequent area(s) of work and specialism(s) reported by ALLD practitioners. Implications for applying the taxonomy are considered from the perspectives of international and national associations, institutions, and individual practitioners.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".