Identifying typical academic language and learning development practitioner roles and specialisms: an international taxonomy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it