Speaking the Same Language: Relevance for a Global Ontology of Work-Integrated Learning
Bibliographic record
Abstract
This paper looks at differing perceptions of aspects of work-integrated learning (WIL) in the global context, and discusses the relevancy for establishing an ontology for not only a common ‘language’, but a common ‘culture ’ and ‘identity ’ in WIL as well. Le Page and Tabouret-Keller (1985) note that people mark their identity through language: This paper will demonstrate, as a linguistic case example, the use of ‘Ocker Strine ’ (Australian Colloquial English) in Australia and how specific cultural usage of terminology can co-exist with a standardized global format, with the issue of a WIL ‘identity ’ being emphasized. As a point of review, the findings of an audit of WIL practice within a single Australian university Faculty of business – the Griffith Business School – will be presented as a case example. Although higher education has become an industry and industry sector in its own right (Bennett, 2008), it has been posited that universities are going through a period of re-invention and should regard themselves as not exclusively as ‘institutions’, but as ‘communities of higher education ’ (Lee, 1999). Linking directly all stakeholders in this ‘community’, work-integrated learning (WIL) has emerged over the last two decades as an effective complementary teaching and learning methodology (Mintzberg, 2005; Orrell, 2005) and, has expanded beyond its historic academic areas of concentration to include, essentially, every field in higher education (Dewar, 2006). With this global increase in interest in WIL in higher education, so has increased inconsistency in definition and interpretation in practice, structure and especially typology (McGill, 2009). What is one institution’s ‘internship ’ can be another’s ‘co-op’, ‘practicum ’ or ‘field experience’, with practices, procedures, desired outcomes and perception of the experience differing from one part of the world to another, from one higher education institution to another and, even within individual institutions
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.100 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".